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network3.ipynb based on network3.py

from http://neuralnetworksanddeeplearning.com/chap6.html

Libraries

Standard Library





In [1]:



    


import cPickle 
import gzip

Third-party libraries





In [2]:



    


import numpy as np
import theano
import theano.tensor as T
from theano.tensor.nnet import conv
from theano.tensor.nnet import softmax
from theano.tensor import shared_randomstreams
from theano.tensor.signal import downsample



    


















    






/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.
  "downsample module has been moved to the theano.tensor.signal.pool module.")

Activation functions for neurons





In [3]:



    


def linear(z): return z
def ReLU(z): return T.maximum(0.0, z)
from theano.tensor.nnet import sigmoid
from theano.tensor import tanh

Constants





In [4]:



    


GPU = False
if GPU:
    print "Trying to run under a GPU.  If this is not desired, then modify "+\
        "network3.py\nto set the GPU flag to False."
    try: theano.config.device = 'gpu'
    except: pass # it's already set
    theano.config.floatX = 'float32'
else:
    print "Running with a CPU.  If this is not desired, then the modify "+\
        "network3.py to set\nthe GPU flag to True."



    


















    






Running with a CPU.  If this is not desired, then the modify network3.py to set
the GPU flag to True.

Load the MNIST data





In [5]:



    


def load_data_shared(filename="../../neural-networks-and-deep-learning/data/mnist.pkl.gz"):
    f = gzip.open(filename, 'rb')
    training_data, validation_data, test_data = cPickle.load(f)
    f.close()
    def shared(data):
        """Place the data into shared variables.  This allows Theano to copy
        the data to the GPU, if one is available.

        """
        shared_x = theano.shared(
            np.asarray(data[0], dtype=theano.config.floatX), borrow=True)
        shared_y = theano.shared(
            np.asarray(data[1], dtype=theano.config.floatX), borrow=True)
        return shared_x, T.cast(shared_y, "int32")
    return [shared(training_data), shared(validation_data), shared(test_data)]

Main class used to construct and train networks





In [6]:



    


class Network(object):

    def __init__(self, layers, mini_batch_size):
        """Takes a list of `layers`, describing the network architecture, and
        a value for the `mini_batch_size` to be used during training
        by stochastic gradient descent.

        """
        self.layers = layers
        self.mini_batch_size = mini_batch_size
        self.params = [param for layer in self.layers for param in layer.params]
        self.x = T.matrix("x")
        self.y = T.ivector("y")
        init_layer = self.layers[0]
        init_layer.set_inpt(self.x, self.x, self.mini_batch_size)
        for j in xrange(1, len(self.layers)):
            prev_layer, layer  = self.layers[j-1], self.layers[j]
            layer.set_inpt(
                prev_layer.output, prev_layer.output_dropout, self.mini_batch_size)
        self.output = self.layers[-1].output
        self.output_dropout = self.layers[-1].output_dropout

    def SGD(self, training_data, epochs, mini_batch_size, eta,
            validation_data, test_data, lmbda=0.0):
        """Train the network using mini-batch stochastic gradient descent."""
        training_x, training_y = training_data
        validation_x, validation_y = validation_data
        test_x, test_y = test_data

        # compute number of minibatches for training, validation and testing
        num_training_batches = size(training_data)/mini_batch_size
        num_validation_batches = size(validation_data)/mini_batch_size
        num_test_batches = size(test_data)/mini_batch_size

        # define the (regularized) cost function, symbolic gradients, and updates
        l2_norm_squared = sum([(layer.w**2).sum() for layer in self.layers])
        cost = self.layers[-1].cost(self)+\
               0.5*lmbda*l2_norm_squared/num_training_batches
        grads = T.grad(cost, self.params)
        updates = [(param, param-eta*grad)
                   for param, grad in zip(self.params, grads)]

        # define functions to train a mini-batch, and to compute the
        # accuracy in validation and test mini-batches.
        i = T.lscalar() # mini-batch index
        train_mb = theano.function(
            [i], cost, updates=updates,
            givens={
                self.x:
                training_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
                self.y:
                training_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
            })
        validate_mb_accuracy = theano.function(
            [i], self.layers[-1].accuracy(self.y),
            givens={
                self.x:
                validation_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
                self.y:
                validation_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
            })
        test_mb_accuracy = theano.function(
            [i], self.layers[-1].accuracy(self.y),
            givens={
                self.x:
                test_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
                self.y:
                test_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
            })
        self.test_mb_predictions = theano.function(
            [i], self.layers[-1].y_out,
            givens={
                self.x:
                test_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
            })
        # Do the actual training
        best_validation_accuracy = 0.0
        for epoch in xrange(epochs):
            for minibatch_index in xrange(num_training_batches):
                iteration = num_training_batches*epoch+minibatch_index
                if iteration % 1000 == 0:
                    print("Training mini-batch number {0}".format(iteration))
                cost_ij = train_mb(minibatch_index)
                if (iteration+1) % num_training_batches == 0:
                    validation_accuracy = np.mean(
                        [validate_mb_accuracy(j) for j in xrange(num_validation_batches)])
                    print("Epoch {0}: validation accuracy {1:.2%}".format(
                        epoch, validation_accuracy))
                    if validation_accuracy >= best_validation_accuracy:
                        print("This is the best validation accuracy to date.")
                        best_validation_accuracy = validation_accuracy
                        best_iteration = iteration
                        if test_data:
                            test_accuracy = np.mean(
                                [test_mb_accuracy(j) for j in xrange(num_test_batches)])
                            print('The corresponding test accuracy is {0:.2%}'.format(
                                test_accuracy))
        print("Finished training network.")
        print("Best validation accuracy of {0:.2%} obtained at iteration {1}".format(
            best_validation_accuracy, best_iteration))
        print("Corresponding test accuracy of {0:.2%}".format(test_accuracy))

Define layer types





In [7]:



    


class ConvPoolLayer(object):
    """Used to create a combination of a convolutional and a max-pooling
    layer.  A more sophisticated implementation would separate the
    two, but for our purposes we'll always use them together, and it
    simplifies the code, so it makes sense to combine them.

    """

    def __init__(self, filter_shape, image_shape, poolsize=(2, 2),
                 activation_fn=sigmoid):
        """`filter_shape` is a tuple of length 4, whose entries are the number
        of filters, the number of input feature maps, the filter height, and the
        filter width.

        `image_shape` is a tuple of length 4, whose entries are the
        mini-batch size, the number of input feature maps, the image
        height, and the image width.

        `poolsize` is a tuple of length 2, whose entries are the y and
        x pooling sizes.

        """
        self.filter_shape = filter_shape
        self.image_shape = image_shape
        self.poolsize = poolsize
        self.activation_fn=activation_fn
        # initialize weights and biases
        n_out = (filter_shape[0]*np.prod(filter_shape[2:])/np.prod(poolsize))
        self.w = theano.shared(
            np.asarray(
                np.random.normal(loc=0, scale=np.sqrt(1.0/n_out), size=filter_shape),
                dtype=theano.config.floatX),
            borrow=True)
        self.b = theano.shared(
            np.asarray(
                np.random.normal(loc=0, scale=1.0, size=(filter_shape[0],)),
                dtype=theano.config.floatX),
            borrow=True)
        self.params = [self.w, self.b]

    def set_inpt(self, inpt, inpt_dropout, mini_batch_size):
        self.inpt = inpt.reshape(self.image_shape)
        conv_out = conv.conv2d(
            input=self.inpt, filters=self.w, filter_shape=self.filter_shape,
            image_shape=self.image_shape)
        pooled_out = downsample.max_pool_2d(
            input=conv_out, ds=self.poolsize, ignore_border=True)
        self.output = self.activation_fn(
            pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
        self.output_dropout = self.output # no dropout in the convolutional layers



    











In [8]:



    


class FullyConnectedLayer(object):

    def __init__(self, n_in, n_out, activation_fn=sigmoid, p_dropout=0.0):
        self.n_in = n_in
        self.n_out = n_out
        self.activation_fn = activation_fn
        self.p_dropout = p_dropout
        # Initialize weights and biases
        self.w = theano.shared(
            np.asarray(
                np.random.normal(
                    loc=0.0, scale=np.sqrt(1.0/n_out), size=(n_in, n_out)),
                dtype=theano.config.floatX),
            name='w', borrow=True)
        self.b = theano.shared(
            np.asarray(np.random.normal(loc=0.0, scale=1.0, size=(n_out,)),
                       dtype=theano.config.floatX),
            name='b', borrow=True)
        self.params = [self.w, self.b]

    def set_inpt(self, inpt, inpt_dropout, mini_batch_size):
        self.inpt = inpt.reshape((mini_batch_size, self.n_in))
        self.output = self.activation_fn(
            (1-self.p_dropout)*T.dot(self.inpt, self.w) + self.b)
        self.y_out = T.argmax(self.output, axis=1)
        self.inpt_dropout = dropout_layer(
            inpt_dropout.reshape((mini_batch_size, self.n_in)), self.p_dropout)
        self.output_dropout = self.activation_fn(
            T.dot(self.inpt_dropout, self.w) + self.b)

    def accuracy(self, y):
        "Return the accuracy for the mini-batch."
        return T.mean(T.eq(y, self.y_out))



    











In [9]:



    


class SoftmaxLayer(object):

    def __init__(self, n_in, n_out, p_dropout=0.0):
        self.n_in = n_in
        self.n_out = n_out
        self.p_dropout = p_dropout
        # Initialize weights and biases
        self.w = theano.shared(
            np.zeros((n_in, n_out), dtype=theano.config.floatX),
            name='w', borrow=True)
        self.b = theano.shared(
            np.zeros((n_out,), dtype=theano.config.floatX),
            name='b', borrow=True)
        self.params = [self.w, self.b]

    def set_inpt(self, inpt, inpt_dropout, mini_batch_size):
        self.inpt = inpt.reshape((mini_batch_size, self.n_in))
        self.output = softmax((1-self.p_dropout)*T.dot(self.inpt, self.w) + self.b)
        self.y_out = T.argmax(self.output, axis=1)
        self.inpt_dropout = dropout_layer(
            inpt_dropout.reshape((mini_batch_size, self.n_in)), self.p_dropout)
        self.output_dropout = softmax(T.dot(self.inpt_dropout, self.w) + self.b)

    def cost(self, net):
        "Return the log-likelihood cost."
        return -T.mean(T.log(self.output_dropout)[T.arange(net.y.shape[0]), net.y])

    def accuracy(self, y):
        "Return the accuracy for the mini-batch."
        return T.mean(T.eq(y, self.y_out))

Miscellanea





In [10]:



    


def size(data):
    "Return the size of the dataset `data`."
    return data[0].get_value(borrow=True).shape[0]

def dropout_layer(layer, p_dropout):
    srng = shared_randomstreams.RandomStreams(
        np.random.RandomState(0).randint(999999))
    mask = srng.binomial(n=1, p=1-p_dropout, size=layer.shape)
    return layer*T.cast(mask, theano.config.floatX)

Load Data





In [11]:



    


training_data, validation_data, test_data = load_data_shared()

Run shallow architecture (single hidden layer) as baseline





In [12]:



    


mini_batch_size = 10



    











In [13]:



    


net = Network([
        FullyConnectedLayer(n_in=784, n_out=100),
        SoftmaxLayer(n_in=100, n_out=10)
    ], mini_batch_size)



    











In [14]:



    


net.SGD(training_data, 60, mini_batch_size, 0.1, validation_data, test_data)



    


















    






ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(Elemwise{add,no_inplace}.0, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Elemwise{add,no_inplace}.0) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, Elemwise{add,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_canonizer
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{1})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{add,no_inplace}(TensorConstant{1}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(Elemwise{add,no_inplace}.0, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Elemwise{add,no_inplace}.0) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, Elemwise{add,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_canonizer
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{1})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{add,no_inplace}(TensorConstant{1}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(Elemwise{add,no_inplace}.0, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Elemwise{add,no_inplace}.0) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, Elemwise{add,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_canonizer
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{1})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{add,no_inplace}(TensorConstant{1}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second}(AdvancedSubtensor.0, Elemwise{true_div}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{true_div}.0) of Op Alloc(Elemwise{true_div}.0, Elemwise{sub,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(Elemwise{log,no_inplace}.0, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 2 (Shape_i{1}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, TensorConstant{10}, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(Elemwise{log,no_inplace}.0, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 2 (Shape_i{1}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, TensorConstant{10}, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second}(AdvancedSubtensor.0, Elemwise{true_div}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{true_div}.0) of Op Alloc(Elemwise{true_div}.0, Elemwise{sub,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(Elemwise{mul,no_inplace}.0, w)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op Dot22(Elemwise{mul,no_inplace}.0, w) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(Elemwise{mul,no_inplace}.0, w)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op Dot22(Elemwise{mul,no_inplace}.0, w) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(DimShuffle{1,0}.0, SoftmaxGrad.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (DimShuffle{1,0}.0) of Op Dot22(DimShuffle{1,0}.0, SoftmaxGrad.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(SoftmaxGrad.0, w.T)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (SoftmaxGrad.0) of Op Dot22(SoftmaxGrad.0, w.T) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(DimShuffle{1,0}.0, Elemwise{mul}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (DimShuffle{1,0}.0) of Op Dot22(DimShuffle{1,0}.0, Elemwise{mul}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_specialize
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5485, in local_add_specialize
    ret = fill_chain(new_inputs[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5458, in fill_chain
    out = _fill_chain(v, node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 142, in _fill_chain
    new_out = T.fill(i, new_out)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{second,no_inplace}(dot.0, dot.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_specialize
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5485, in local_add_specialize
    ret = fill_chain(new_inputs[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5458, in fill_chain
    out = _fill_chain(v, node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 142, in _fill_chain
    new_out = T.fill(i, new_out)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{second,no_inplace}(dot.0, dot.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_advanced_indexing_crossentropy_onehot_grad
ERROR (theano.gof.opt): node: SoftmaxGrad(Elemwise{true_div}.0, Softmax.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/nnet/nnet.py", line 1907, in local_advanced_indexing_crossentropy_onehot_grad
    out_grad = -incr
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/var.py", line 39, in __neg__
    return theano.tensor.basic.neg(self)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{second}.0) of Op Elemwise{neg,no_inplace}(Elemwise{second}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_specialize
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(TensorConstant{-1.0}, mean)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5427, in local_mul_specialize
    rval = -new_inputs[0]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/var.py", line 39, in __neg__
    return theano.tensor.basic.neg(self)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (mean) of Op Elemwise{neg,no_inplace}(mean) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second}(AdvancedSubtensor.0, Elemwise{true_div}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{true_div}.0) of Op Alloc(Elemwise{true_div}.0, Elemwise{sub,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(Elemwise{log,no_inplace}.0, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 2 (Shape_i{1}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, TensorConstant{10}, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_advanced_indexing_crossentropy_onehot
ERROR (theano.gof.opt): node: AdvancedSubtensor(Elemwise{log,no_inplace}.0, ARange{dtype='int64'}.0, Elemwise{Cast{int32}}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/nnet/nnet.py", line 1692, in local_advanced_indexing_crossentropy_onehot
    b_var = tensor.zeros_like(x_var[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/var.py", line 532, in __getitem__
    lambda entry: isinstance(entry, Variable)))
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{add,no_inplace}.0) of Op Subtensor{int64}(Elemwise{add,no_inplace}.0, Constant{0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_logsoftmax
ERROR (theano.gof.opt): node: Elemwise{log,no_inplace}(Softmax.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/nnet/nnet.py", line 756, in local_logsoftmax
    ret = new_op(inVars)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{add,no_inplace}.0) of Op LogSoftmax(Elemwise{add,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): SeqOptimizer apply <theano.tensor.opt.FusionOptimizer object at 0x7f4ee231a9d0>
ERROR (theano.gof.opt): Traceback:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 230, in apply
    sub_prof = optimizer.optimize(fgraph)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 89, in optimize
    ret = self.apply(fgraph, *args, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 6658, in apply
    new_outputs = self.optimizer(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 6727, in local_add_mul_fusion
    output_node = node.op(*(l + inp.owner.inputs))
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{sub}.0) of Op Elemwise{mul}(Elemwise{sub}.0, Reshape{2}.0, sigmoid.0) missing default value

ERROR (theano.gof.opt): SeqOptimizer apply <theano.tensor.opt.FusionOptimizer object at 0x7f4ee231aa90>
ERROR (theano.gof.opt): Traceback:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 230, in apply
    sub_prof = optimizer.optimize(fgraph)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 89, in optimize
    ret = self.apply(fgraph, *args, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 6658, in apply
    new_outputs = self.optimizer(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 6502, in local_fuse
    return_list=True)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<float64>) of Op mul(<float64>, <float64>) missing default value

ERROR (theano.gof.opt): SeqOptimizer apply <theano.compile.mode.AddDestroyHandler object at 0x7f4f0d7aba10>
ERROR (theano.gof.opt): Traceback:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 230, in apply
    sub_prof = optimizer.optimize(fgraph)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 89, in optimize
    ret = self.apply(fgraph, *args, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/compile/mode.py", line 134, in apply
    fgraph.replace_validate(o, _output_guard(o),
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op OutputGuard(Elemwise{mul,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0}(Elemwise{mul,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op InplaceDimShuffle{1,0}(Elemwise{mul,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0}(Elemwise{mul,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op InplaceDimShuffle{1,0}(Elemwise{mul,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{x}(Elemwise{Cast{float64}}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{Cast{float64}}.0) of Op InplaceDimShuffle{x}(Elemwise{Cast{float64}}.0) missing default value











    






Training mini-batch number 0
Training mini-batch number 1000
Training mini-batch number 2000
Training mini-batch number 3000
Training mini-batch number 4000
Epoch 0: validation accuracy 92.41%
This is the best validation accuracy to date.
The corresponding test accuracy is 91.67%
Training mini-batch number 5000
Training mini-batch number 6000
Training mini-batch number 7000
Training mini-batch number 8000
Training mini-batch number 9000
Epoch 1: validation accuracy 94.67%
This is the best validation accuracy to date.
The corresponding test accuracy is 94.01%
Training mini-batch number 10000
Training mini-batch number 11000
Training mini-batch number 12000
Training mini-batch number 13000
Training mini-batch number 14000
Epoch 2: validation accuracy 95.79%
This is the best validation accuracy to date.
The corresponding test accuracy is 95.09%
Training mini-batch number 15000
Training mini-batch number 16000
Training mini-batch number 17000
Training mini-batch number 18000
Training mini-batch number 19000
Epoch 3: validation accuracy 96.28%
This is the best validation accuracy to date.
The corresponding test accuracy is 95.69%
Training mini-batch number 20000
Training mini-batch number 21000
Training mini-batch number 22000
Training mini-batch number 23000
Training mini-batch number 24000
Epoch 4: validation accuracy 96.67%
This is the best validation accuracy to date.
The corresponding test accuracy is 96.17%
Training mini-batch number 25000
Training mini-batch number 26000
Training mini-batch number 27000
Training mini-batch number 28000
Training mini-batch number 29000
Epoch 5: validation accuracy 96.87%
This is the best validation accuracy to date.
The corresponding test accuracy is 96.51%
Training mini-batch number 30000
Training mini-batch number 31000
Training mini-batch number 32000
Training mini-batch number 33000
Training mini-batch number 34000
Epoch 6: validation accuracy 96.95%
This is the best validation accuracy to date.
The corresponding test accuracy is 96.75%
Training mini-batch number 35000
Training mini-batch number 36000
Training mini-batch number 37000
Training mini-batch number 38000
Training mini-batch number 39000
Epoch 7: validation accuracy 97.10%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.05%
Training mini-batch number 40000
Training mini-batch number 41000
Training mini-batch number 42000
Training mini-batch number 43000
Training mini-batch number 44000
Epoch 8: validation accuracy 97.30%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.22%
Training mini-batch number 45000
Training mini-batch number 46000
Training mini-batch number 47000
Training mini-batch number 48000
Training mini-batch number 49000
Epoch 9: validation accuracy 97.40%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.31%
Training mini-batch number 50000
Training mini-batch number 51000
Training mini-batch number 52000
Training mini-batch number 53000
Training mini-batch number 54000
Epoch 10: validation accuracy 97.47%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.38%
Training mini-batch number 55000
Training mini-batch number 56000
Training mini-batch number 57000
Training mini-batch number 58000
Training mini-batch number 59000
Epoch 11: validation accuracy 97.51%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.41%
Training mini-batch number 60000
Training mini-batch number 61000
Training mini-batch number 62000
Training mini-batch number 63000
Training mini-batch number 64000
Epoch 12: validation accuracy 97.50%
Training mini-batch number 65000
Training mini-batch number 66000
Training mini-batch number 67000
Training mini-batch number 68000
Training mini-batch number 69000
Epoch 13: validation accuracy 97.52%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.48%
Training mini-batch number 70000
Training mini-batch number 71000
Training mini-batch number 72000
Training mini-batch number 73000
Training mini-batch number 74000
Epoch 14: validation accuracy 97.59%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.57%
Training mini-batch number 75000
Training mini-batch number 76000
Training mini-batch number 77000
Training mini-batch number 78000
Training mini-batch number 79000
Epoch 15: validation accuracy 97.59%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.62%
Training mini-batch number 80000
Training mini-batch number 81000
Training mini-batch number 82000
Training mini-batch number 83000
Training mini-batch number 84000
Epoch 16: validation accuracy 97.56%
Training mini-batch number 85000
Training mini-batch number 86000
Training mini-batch number 87000
Training mini-batch number 88000
Training mini-batch number 89000
Epoch 17: validation accuracy 97.58%
Training mini-batch number 90000
Training mini-batch number 91000
Training mini-batch number 92000
Training mini-batch number 93000
Training mini-batch number 94000
Epoch 18: validation accuracy 97.64%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.68%
Training mini-batch number 95000
Training mini-batch number 96000
Training mini-batch number 97000
Training mini-batch number 98000
Training mini-batch number 99000
Epoch 19: validation accuracy 97.62%
Training mini-batch number 100000
Training mini-batch number 101000
Training mini-batch number 102000
Training mini-batch number 103000
Training mini-batch number 104000
Epoch 20: validation accuracy 97.64%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.70%
Training mini-batch number 105000
Training mini-batch number 106000
Training mini-batch number 107000
Training mini-batch number 108000
Training mini-batch number 109000
Epoch 21: validation accuracy 97.64%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.72%
Training mini-batch number 110000
Training mini-batch number 111000
Training mini-batch number 112000
Training mini-batch number 113000
Training mini-batch number 114000
Epoch 22: validation accuracy 97.66%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.74%
Training mini-batch number 115000
Training mini-batch number 116000
Training mini-batch number 117000
Training mini-batch number 118000
Training mini-batch number 119000
Epoch 23: validation accuracy 97.69%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.76%
Training mini-batch number 120000
Training mini-batch number 121000
Training mini-batch number 122000
Training mini-batch number 123000
Training mini-batch number 124000
Epoch 24: validation accuracy 97.69%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.77%
Training mini-batch number 125000
Training mini-batch number 126000
Training mini-batch number 127000
Training mini-batch number 128000
Training mini-batch number 129000
Epoch 25: validation accuracy 97.69%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.80%
Training mini-batch number 130000
Training mini-batch number 131000
Training mini-batch number 132000
Training mini-batch number 133000
Training mini-batch number 134000
Epoch 26: validation accuracy 97.67%
Training mini-batch number 135000
Training mini-batch number 136000
Training mini-batch number 137000
Training mini-batch number 138000
Training mini-batch number 139000
Epoch 27: validation accuracy 97.69%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.80%
Training mini-batch number 140000
Training mini-batch number 141000
Training mini-batch number 142000
Training mini-batch number 143000
Training mini-batch number 144000
Epoch 28: validation accuracy 97.71%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.81%
Training mini-batch number 145000
Training mini-batch number 146000
Training mini-batch number 147000
Training mini-batch number 148000
Training mini-batch number 149000
Epoch 29: validation accuracy 97.70%
Training mini-batch number 150000
Training mini-batch number 151000
Training mini-batch number 152000
Training mini-batch number 153000
Training mini-batch number 154000
Epoch 30: validation accuracy 97.71%
Training mini-batch number 155000
Training mini-batch number 156000
Training mini-batch number 157000
Training mini-batch number 158000
Training mini-batch number 159000
Epoch 31: validation accuracy 97.71%
Training mini-batch number 160000
Training mini-batch number 161000
Training mini-batch number 162000
Training mini-batch number 163000
Training mini-batch number 164000
Epoch 32: validation accuracy 97.72%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.80%
Training mini-batch number 165000
Training mini-batch number 166000
Training mini-batch number 167000
Training mini-batch number 168000
Training mini-batch number 169000
Epoch 33: validation accuracy 97.72%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.82%
Training mini-batch number 170000
Training mini-batch number 171000
Training mini-batch number 172000
Training mini-batch number 173000
Training mini-batch number 174000
Epoch 34: validation accuracy 97.72%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.79%
Training mini-batch number 175000
Training mini-batch number 176000
Training mini-batch number 177000
Training mini-batch number 178000
Training mini-batch number 179000
Epoch 35: validation accuracy 97.72%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.78%
Training mini-batch number 180000
Training mini-batch number 181000
Training mini-batch number 182000
Training mini-batch number 183000
Training mini-batch number 184000
Epoch 36: validation accuracy 97.74%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.81%
Training mini-batch number 185000
Training mini-batch number 186000
Training mini-batch number 187000
Training mini-batch number 188000
Training mini-batch number 189000
Epoch 37: validation accuracy 97.74%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.81%
Training mini-batch number 190000
Training mini-batch number 191000
Training mini-batch number 192000
Training mini-batch number 193000
Training mini-batch number 194000
Epoch 38: validation accuracy 97.75%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.80%
Training mini-batch number 195000
Training mini-batch number 196000
Training mini-batch number 197000
Training mini-batch number 198000
Training mini-batch number 199000
Epoch 39: validation accuracy 97.77%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.80%
Training mini-batch number 200000
Training mini-batch number 201000
Training mini-batch number 202000
Training mini-batch number 203000
Training mini-batch number 204000
Epoch 40: validation accuracy 97.78%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.79%
Training mini-batch number 205000
Training mini-batch number 206000
Training mini-batch number 207000
Training mini-batch number 208000
Training mini-batch number 209000
Epoch 41: validation accuracy 97.77%
Training mini-batch number 210000
Training mini-batch number 211000
Training mini-batch number 212000
Training mini-batch number 213000
Training mini-batch number 214000
Epoch 42: validation accuracy 97.80%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.82%
Training mini-batch number 215000
Training mini-batch number 216000
Training mini-batch number 217000
Training mini-batch number 218000
Training mini-batch number 219000
Epoch 43: validation accuracy 97.79%
Training mini-batch number 220000
Training mini-batch number 221000
Training mini-batch number 222000
Training mini-batch number 223000
Training mini-batch number 224000
Epoch 44: validation accuracy 97.78%
Training mini-batch number 225000
Training mini-batch number 226000
Training mini-batch number 227000
Training mini-batch number 228000
Training mini-batch number 229000
Epoch 45: validation accuracy 97.79%
Training mini-batch number 230000
Training mini-batch number 231000
Training mini-batch number 232000
Training mini-batch number 233000
Training mini-batch number 234000
Epoch 46: validation accuracy 97.80%
Training mini-batch number 235000
Training mini-batch number 236000
Training mini-batch number 237000
Training mini-batch number 238000
Training mini-batch number 239000
Epoch 47: validation accuracy 97.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.82%
Training mini-batch number 240000
Training mini-batch number 241000
Training mini-batch number 242000
Training mini-batch number 243000
Training mini-batch number 244000
Epoch 48: validation accuracy 97.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.81%
Training mini-batch number 245000
Training mini-batch number 246000
Training mini-batch number 247000
Training mini-batch number 248000
Training mini-batch number 249000
Epoch 49: validation accuracy 97.79%
Training mini-batch number 250000
Training mini-batch number 251000
Training mini-batch number 252000
Training mini-batch number 253000
Training mini-batch number 254000
Epoch 50: validation accuracy 97.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.81%
Training mini-batch number 255000
Training mini-batch number 256000
Training mini-batch number 257000
Training mini-batch number 258000
Training mini-batch number 259000
Epoch 51: validation accuracy 97.80%
Training mini-batch number 260000
Training mini-batch number 261000
Training mini-batch number 262000
Training mini-batch number 263000
Training mini-batch number 264000
Epoch 52: validation accuracy 97.80%
Training mini-batch number 265000
Training mini-batch number 266000
Training mini-batch number 267000
Training mini-batch number 268000
Training mini-batch number 269000
Epoch 53: validation accuracy 97.80%
Training mini-batch number 270000
Training mini-batch number 271000
Training mini-batch number 272000
Training mini-batch number 273000
Training mini-batch number 274000
Epoch 54: validation accuracy 97.79%
Training mini-batch number 275000
Training mini-batch number 276000
Training mini-batch number 277000
Training mini-batch number 278000
Training mini-batch number 279000
Epoch 55: validation accuracy 97.80%
Training mini-batch number 280000
Training mini-batch number 281000
Training mini-batch number 282000
Training mini-batch number 283000
Training mini-batch number 284000
Epoch 56: validation accuracy 97.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.84%
Training mini-batch number 285000
Training mini-batch number 286000
Training mini-batch number 287000
Training mini-batch number 288000
Training mini-batch number 289000
Epoch 57: validation accuracy 97.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.82%
Training mini-batch number 290000
Training mini-batch number 291000
Training mini-batch number 292000
Training mini-batch number 293000
Training mini-batch number 294000
Epoch 58: validation accuracy 97.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.85%
Training mini-batch number 295000
Training mini-batch number 296000
Training mini-batch number 297000
Training mini-batch number 298000
Training mini-batch number 299000
Epoch 59: validation accuracy 97.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.85%
Finished training network.
Best validation accuracy of 97.81% obtained at iteration 299999
Corresponding test accuracy of 97.85%

















In [15]:



    


# add a convolutional layer: 
net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
                     filter_shape=(20, 1, 5, 5),
                     poolsize=(2, 2)),
        FullyConnectedLayer(n_in=20*12*12, n_out=100),
        SoftmaxLayer(n_in=100, n_out=10)], 
              mini_batch_size)



    











In [16]:



    


net.SGD(training_data, 60, mini_batch_size, 0.1, validation_data, test_data)



    


















    






Training mini-batch number 0
Training mini-batch number 1000
Training mini-batch number 2000
Training mini-batch number 3000
Training mini-batch number 4000
Epoch 0: validation accuracy 93.56%
This is the best validation accuracy to date.
The corresponding test accuracy is 92.74%
Training mini-batch number 5000
Training mini-batch number 6000
Training mini-batch number 7000
Training mini-batch number 8000
Training mini-batch number 9000
Epoch 1: validation accuracy 96.03%
This is the best validation accuracy to date.
The corresponding test accuracy is 95.51%
Training mini-batch number 10000
Training mini-batch number 11000
Training mini-batch number 12000
Training mini-batch number 13000
Training mini-batch number 14000
Epoch 2: validation accuracy 96.96%
This is the best validation accuracy to date.
The corresponding test accuracy is 96.57%
Training mini-batch number 15000
Training mini-batch number 16000
Training mini-batch number 17000
Training mini-batch number 18000
Training mini-batch number 19000
Epoch 3: validation accuracy 97.41%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.10%
Training mini-batch number 20000
Training mini-batch number 21000
Training mini-batch number 22000
Training mini-batch number 23000
Training mini-batch number 24000
Epoch 4: validation accuracy 97.80%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.47%
Training mini-batch number 25000
Training mini-batch number 26000
Training mini-batch number 27000
Training mini-batch number 28000
Training mini-batch number 29000
Epoch 5: validation accuracy 97.99%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.73%
Training mini-batch number 30000
Training mini-batch number 31000
Training mini-batch number 32000
Training mini-batch number 33000
Training mini-batch number 34000
Epoch 6: validation accuracy 98.00%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.83%
Training mini-batch number 35000
Training mini-batch number 36000
Training mini-batch number 37000
Training mini-batch number 38000
Training mini-batch number 39000
Epoch 7: validation accuracy 98.14%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.88%
Training mini-batch number 40000
Training mini-batch number 41000
Training mini-batch number 42000
Training mini-batch number 43000
Training mini-batch number 44000
Epoch 8: validation accuracy 98.25%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.07%
Training mini-batch number 45000
Training mini-batch number 46000
Training mini-batch number 47000
Training mini-batch number 48000
Training mini-batch number 49000
Epoch 9: validation accuracy 98.40%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.24%
Training mini-batch number 50000
Training mini-batch number 51000
Training mini-batch number 52000
Training mini-batch number 53000
Training mini-batch number 54000
Epoch 10: validation accuracy 98.52%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.31%
Training mini-batch number 55000
Training mini-batch number 56000
Training mini-batch number 57000
Training mini-batch number 58000
Training mini-batch number 59000
Epoch 11: validation accuracy 98.58%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.43%
Training mini-batch number 60000
Training mini-batch number 61000
Training mini-batch number 62000
Training mini-batch number 63000
Training mini-batch number 64000
Epoch 12: validation accuracy 98.61%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.51%
Training mini-batch number 65000
Training mini-batch number 66000
Training mini-batch number 67000
Training mini-batch number 68000
Training mini-batch number 69000
Epoch 13: validation accuracy 98.66%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.58%
Training mini-batch number 70000
Training mini-batch number 71000
Training mini-batch number 72000
Training mini-batch number 73000
Training mini-batch number 74000
Epoch 14: validation accuracy 98.66%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.67%
Training mini-batch number 75000
Training mini-batch number 76000
Training mini-batch number 77000
Training mini-batch number 78000
Training mini-batch number 79000
Epoch 15: validation accuracy 98.68%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.69%
Training mini-batch number 80000
Training mini-batch number 81000
Training mini-batch number 82000
Training mini-batch number 83000
Training mini-batch number 84000
Epoch 16: validation accuracy 98.69%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.76%
Training mini-batch number 85000
Training mini-batch number 86000
Training mini-batch number 87000
Training mini-batch number 88000
Training mini-batch number 89000
Epoch 17: validation accuracy 98.68%
Training mini-batch number 90000
Training mini-batch number 91000
Training mini-batch number 92000
Training mini-batch number 93000
Training mini-batch number 94000
Epoch 18: validation accuracy 98.71%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.74%
Training mini-batch number 95000
Training mini-batch number 96000
Training mini-batch number 97000
Training mini-batch number 98000
Training mini-batch number 99000
Epoch 19: validation accuracy 98.71%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.74%
Training mini-batch number 100000
Training mini-batch number 101000
Training mini-batch number 102000
Training mini-batch number 103000
Training mini-batch number 104000
Epoch 20: validation accuracy 98.73%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.74%
Training mini-batch number 105000
Training mini-batch number 106000
Training mini-batch number 107000
Training mini-batch number 108000
Training mini-batch number 109000
Epoch 21: validation accuracy 98.74%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.78%
Training mini-batch number 110000
Training mini-batch number 111000
Training mini-batch number 112000
Training mini-batch number 113000
Training mini-batch number 114000
Epoch 22: validation accuracy 98.75%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.78%
Training mini-batch number 115000
Training mini-batch number 116000
Training mini-batch number 117000
Training mini-batch number 118000
Training mini-batch number 119000
Epoch 23: validation accuracy 98.73%
Training mini-batch number 120000
Training mini-batch number 121000
Training mini-batch number 122000
Training mini-batch number 123000
Training mini-batch number 124000
Epoch 24: validation accuracy 98.75%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.81%
Training mini-batch number 125000
Training mini-batch number 126000
Training mini-batch number 127000
Training mini-batch number 128000
Training mini-batch number 129000
Epoch 25: validation accuracy 98.78%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.80%
Training mini-batch number 130000
Training mini-batch number 131000
Training mini-batch number 132000
Training mini-batch number 133000
Training mini-batch number 134000
Epoch 26: validation accuracy 98.76%
Training mini-batch number 135000
Training mini-batch number 136000
Training mini-batch number 137000
Training mini-batch number 138000
Training mini-batch number 139000
Epoch 27: validation accuracy 98.75%
Training mini-batch number 140000
Training mini-batch number 141000
Training mini-batch number 142000
Training mini-batch number 143000
Training mini-batch number 144000
Epoch 28: validation accuracy 98.74%
Training mini-batch number 145000
Training mini-batch number 146000
Training mini-batch number 147000
Training mini-batch number 148000
Training mini-batch number 149000
Epoch 29: validation accuracy 98.75%
Training mini-batch number 150000
Training mini-batch number 151000
Training mini-batch number 152000
Training mini-batch number 153000
Training mini-batch number 154000
Epoch 30: validation accuracy 98.75%
Training mini-batch number 155000
Training mini-batch number 156000
Training mini-batch number 157000
Training mini-batch number 158000
Training mini-batch number 159000
Epoch 31: validation accuracy 98.75%
Training mini-batch number 160000
Training mini-batch number 161000
Training mini-batch number 162000
Training mini-batch number 163000
Training mini-batch number 164000
Epoch 32: validation accuracy 98.76%
Training mini-batch number 165000
Training mini-batch number 166000
Training mini-batch number 167000
Training mini-batch number 168000
Training mini-batch number 169000
Epoch 33: validation accuracy 98.76%
Training mini-batch number 170000
Training mini-batch number 171000
Training mini-batch number 172000
Training mini-batch number 173000
Training mini-batch number 174000
Epoch 34: validation accuracy 98.76%
Training mini-batch number 175000
Training mini-batch number 176000
Training mini-batch number 177000
Training mini-batch number 178000
Training mini-batch number 179000
Epoch 35: validation accuracy 98.75%
Training mini-batch number 180000
Training mini-batch number 181000
Training mini-batch number 182000
Training mini-batch number 183000
Training mini-batch number 184000
Epoch 36: validation accuracy 98.75%
Training mini-batch number 185000
Training mini-batch number 186000
Training mini-batch number 187000
Training mini-batch number 188000
Training mini-batch number 189000
Epoch 37: validation accuracy 98.75%
Training mini-batch number 190000
Training mini-batch number 191000
Training mini-batch number 192000
Training mini-batch number 193000
Training mini-batch number 194000
Epoch 38: validation accuracy 98.76%
Training mini-batch number 195000
Training mini-batch number 196000
Training mini-batch number 197000
Training mini-batch number 198000
Training mini-batch number 199000
Epoch 39: validation accuracy 98.75%
Training mini-batch number 200000
Training mini-batch number 201000
Training mini-batch number 202000
Training mini-batch number 203000
Training mini-batch number 204000
Epoch 40: validation accuracy 98.75%
Training mini-batch number 205000
Training mini-batch number 206000
Training mini-batch number 207000
Training mini-batch number 208000
Training mini-batch number 209000
Epoch 41: validation accuracy 98.76%
Training mini-batch number 210000
Training mini-batch number 211000
Training mini-batch number 212000
Training mini-batch number 213000
Training mini-batch number 214000
Epoch 42: validation accuracy 98.76%
Training mini-batch number 215000
Training mini-batch number 216000
Training mini-batch number 217000
Training mini-batch number 218000
Training mini-batch number 219000
Epoch 43: validation accuracy 98.77%
Training mini-batch number 220000
Training mini-batch number 221000
Training mini-batch number 222000
Training mini-batch number 223000
Training mini-batch number 224000
Epoch 44: validation accuracy 98.77%
Training mini-batch number 225000
Training mini-batch number 226000
Training mini-batch number 227000
Training mini-batch number 228000
Training mini-batch number 229000
Epoch 45: validation accuracy 98.77%
Training mini-batch number 230000
Training mini-batch number 231000
Training mini-batch number 232000
Training mini-batch number 233000
Training mini-batch number 234000
Epoch 46: validation accuracy 98.78%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.90%
Training mini-batch number 235000
Training mini-batch number 236000
Training mini-batch number 237000
Training mini-batch number 238000
Training mini-batch number 239000
Epoch 47: validation accuracy 98.79%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.90%
Training mini-batch number 240000
Training mini-batch number 241000
Training mini-batch number 242000
Training mini-batch number 243000
Training mini-batch number 244000
Epoch 48: validation accuracy 98.79%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.90%
Training mini-batch number 245000
Training mini-batch number 246000
Training mini-batch number 247000
Training mini-batch number 248000
Training mini-batch number 249000
Epoch 49: validation accuracy 98.78%
Training mini-batch number 250000
Training mini-batch number 251000
Training mini-batch number 252000
Training mini-batch number 253000
Training mini-batch number 254000
Epoch 50: validation accuracy 98.80%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.92%
Training mini-batch number 255000
Training mini-batch number 256000
Training mini-batch number 257000
Training mini-batch number 258000
Training mini-batch number 259000
Epoch 51: validation accuracy 98.80%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.93%
Training mini-batch number 260000
Training mini-batch number 261000
Training mini-batch number 262000
Training mini-batch number 263000
Training mini-batch number 264000
Epoch 52: validation accuracy 98.80%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.95%
Training mini-batch number 265000
Training mini-batch number 266000
Training mini-batch number 267000
Training mini-batch number 268000
Training mini-batch number 269000
Epoch 53: validation accuracy 98.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.95%
Training mini-batch number 270000
Training mini-batch number 271000
Training mini-batch number 272000
Training mini-batch number 273000
Training mini-batch number 274000
Epoch 54: validation accuracy 98.81%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.95%
Training mini-batch number 275000
Training mini-batch number 276000
Training mini-batch number 277000
Training mini-batch number 278000
Training mini-batch number 279000
Epoch 55: validation accuracy 98.80%
Training mini-batch number 280000
Training mini-batch number 281000
Training mini-batch number 282000
Training mini-batch number 283000
Training mini-batch number 284000
Epoch 56: validation accuracy 98.80%
Training mini-batch number 285000
Training mini-batch number 286000
Training mini-batch number 287000
Training mini-batch number 288000
Training mini-batch number 289000
Epoch 57: validation accuracy 98.82%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.95%
Training mini-batch number 290000
Training mini-batch number 291000
Training mini-batch number 292000
Training mini-batch number 293000
Training mini-batch number 294000
Epoch 58: validation accuracy 98.82%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.93%
Training mini-batch number 295000
Training mini-batch number 296000
Training mini-batch number 297000
Training mini-batch number 298000
Training mini-batch number 299000
Epoch 59: validation accuracy 98.82%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.92%
Finished training network.
Best validation accuracy of 98.82% obtained at iteration 299999
Corresponding test accuracy of 98.92%

















In [17]:



    


# remove fully connected layer: 
net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
                     filter_shape=(20, 1, 5, 5), 
                     poolsize=(2, 2)),
        # FullyConnectedLayer(n_in=20*12*12, n_out=100),
        SoftmaxLayer(n_in=20*12*12, n_out=10)], 
              mini_batch_size)



    











In [18]:



    


net.SGD(training_data, 60, mini_batch_size, 0.1, validation_data, test_data)



    


















    






Training mini-batch number 0
Training mini-batch number 1000
Training mini-batch number 2000
Training mini-batch number 3000
Training mini-batch number 4000
Epoch 0: validation accuracy 94.05%
This is the best validation accuracy to date.
The corresponding test accuracy is 93.53%
Training mini-batch number 5000
Training mini-batch number 6000
Training mini-batch number 7000
Training mini-batch number 8000
Training mini-batch number 9000
Epoch 1: validation accuracy 96.88%
This is the best validation accuracy to date.
The corresponding test accuracy is 96.58%
Training mini-batch number 10000
Training mini-batch number 11000
Training mini-batch number 12000
Training mini-batch number 13000
Training mini-batch number 14000
Epoch 2: validation accuracy 97.62%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.52%
Training mini-batch number 15000
Training mini-batch number 16000
Training mini-batch number 17000
Training mini-batch number 18000
Training mini-batch number 19000
Epoch 3: validation accuracy 98.00%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.80%
Training mini-batch number 20000
Training mini-batch number 21000
Training mini-batch number 22000
Training mini-batch number 23000
Training mini-batch number 24000
Epoch 4: validation accuracy 98.16%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.99%
Training mini-batch number 25000
Training mini-batch number 26000
Training mini-batch number 27000
Training mini-batch number 28000
Training mini-batch number 29000
Epoch 5: validation accuracy 98.22%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.10%
Training mini-batch number 30000
Training mini-batch number 31000
Training mini-batch number 32000
Training mini-batch number 33000
Training mini-batch number 34000
Epoch 6: validation accuracy 98.30%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.19%
Training mini-batch number 35000
Training mini-batch number 36000
Training mini-batch number 37000
Training mini-batch number 38000
Training mini-batch number 39000
Epoch 7: validation accuracy 98.37%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.20%
Training mini-batch number 40000
Training mini-batch number 41000
Training mini-batch number 42000
Training mini-batch number 43000
Training mini-batch number 44000
Epoch 8: validation accuracy 98.43%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.26%
Training mini-batch number 45000
Training mini-batch number 46000
Training mini-batch number 47000
Training mini-batch number 48000
Training mini-batch number 49000
Epoch 9: validation accuracy 98.46%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.33%
Training mini-batch number 50000
Training mini-batch number 51000
Training mini-batch number 52000
Training mini-batch number 53000
Training mini-batch number 54000
Epoch 10: validation accuracy 98.44%
Training mini-batch number 55000
Training mini-batch number 56000
Training mini-batch number 57000
Training mini-batch number 58000
Training mini-batch number 59000
Epoch 11: validation accuracy 98.50%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.34%
Training mini-batch number 60000
Training mini-batch number 61000
Training mini-batch number 62000
Training mini-batch number 63000
Training mini-batch number 64000
Epoch 12: validation accuracy 98.54%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.34%
Training mini-batch number 65000
Training mini-batch number 66000
Training mini-batch number 67000
Training mini-batch number 68000
Training mini-batch number 69000
Epoch 13: validation accuracy 98.55%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.36%
Training mini-batch number 70000
Training mini-batch number 71000
Training mini-batch number 72000
Training mini-batch number 73000
Training mini-batch number 74000
Epoch 14: validation accuracy 98.54%
Training mini-batch number 75000
Training mini-batch number 76000
Training mini-batch number 77000
Training mini-batch number 78000
Training mini-batch number 79000
Epoch 15: validation accuracy 98.57%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.39%
Training mini-batch number 80000
Training mini-batch number 81000
Training mini-batch number 82000
Training mini-batch number 83000
Training mini-batch number 84000
Epoch 16: validation accuracy 98.55%
Training mini-batch number 85000
Training mini-batch number 86000
Training mini-batch number 87000
Training mini-batch number 88000
Training mini-batch number 89000
Epoch 17: validation accuracy 98.57%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.43%
Training mini-batch number 90000
Training mini-batch number 91000
Training mini-batch number 92000
Training mini-batch number 93000
Training mini-batch number 94000
Epoch 18: validation accuracy 98.60%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.42%
Training mini-batch number 95000
Training mini-batch number 96000
Training mini-batch number 97000
Training mini-batch number 98000
Training mini-batch number 99000
Epoch 19: validation accuracy 98.61%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.43%
Training mini-batch number 100000
Training mini-batch number 101000
Training mini-batch number 102000
Training mini-batch number 103000
Training mini-batch number 104000
Epoch 20: validation accuracy 98.61%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.43%
Training mini-batch number 105000
Training mini-batch number 106000
Training mini-batch number 107000
Training mini-batch number 108000
Training mini-batch number 109000
Epoch 21: validation accuracy 98.61%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.47%
Training mini-batch number 110000
Training mini-batch number 111000
Training mini-batch number 112000
Training mini-batch number 113000
Training mini-batch number 114000
Epoch 22: validation accuracy 98.60%
Training mini-batch number 115000
Training mini-batch number 116000
Training mini-batch number 117000
Training mini-batch number 118000
Training mini-batch number 119000
Epoch 23: validation accuracy 98.60%
Training mini-batch number 120000
Training mini-batch number 121000
Training mini-batch number 122000
Training mini-batch number 123000
Training mini-batch number 124000
Epoch 24: validation accuracy 98.59%
Training mini-batch number 125000
Training mini-batch number 126000
Training mini-batch number 127000
Training mini-batch number 128000
Training mini-batch number 129000
Epoch 25: validation accuracy 98.58%
Training mini-batch number 130000
Training mini-batch number 131000
Training mini-batch number 132000
Training mini-batch number 133000
Training mini-batch number 134000
Epoch 26: validation accuracy 98.59%
Training mini-batch number 135000
Training mini-batch number 136000
Training mini-batch number 137000
Training mini-batch number 138000
Training mini-batch number 139000
Epoch 27: validation accuracy 98.58%
Training mini-batch number 140000
Training mini-batch number 141000
Training mini-batch number 142000
Training mini-batch number 143000
Training mini-batch number 144000
Epoch 28: validation accuracy 98.59%
Training mini-batch number 145000
Training mini-batch number 146000
Training mini-batch number 147000
Training mini-batch number 148000
Training mini-batch number 149000
Epoch 29: validation accuracy 98.58%
Training mini-batch number 150000
Training mini-batch number 151000
Training mini-batch number 152000
Training mini-batch number 153000
Training mini-batch number 154000
Epoch 30: validation accuracy 98.58%
Training mini-batch number 155000
Training mini-batch number 156000
Training mini-batch number 157000
Training mini-batch number 158000
Training mini-batch number 159000
Epoch 31: validation accuracy 98.57%
Training mini-batch number 160000
Training mini-batch number 161000
Training mini-batch number 162000
Training mini-batch number 163000
Training mini-batch number 164000
Epoch 32: validation accuracy 98.57%
Training mini-batch number 165000
Training mini-batch number 166000
Training mini-batch number 167000
Training mini-batch number 168000
Training mini-batch number 169000
Epoch 33: validation accuracy 98.57%
Training mini-batch number 170000
Training mini-batch number 171000
Training mini-batch number 172000
Training mini-batch number 173000
Training mini-batch number 174000
Epoch 34: validation accuracy 98.55%
Training mini-batch number 175000
Training mini-batch number 176000
Training mini-batch number 177000
Training mini-batch number 178000
Training mini-batch number 179000
Epoch 35: validation accuracy 98.53%
Training mini-batch number 180000
Training mini-batch number 181000
Training mini-batch number 182000
Training mini-batch number 183000
Training mini-batch number 184000
Epoch 36: validation accuracy 98.50%
Training mini-batch number 185000
Training mini-batch number 186000
Training mini-batch number 187000
Training mini-batch number 188000
Training mini-batch number 189000
Epoch 37: validation accuracy 98.50%
Training mini-batch number 190000
Training mini-batch number 191000
Training mini-batch number 192000
Training mini-batch number 193000
Training mini-batch number 194000
Epoch 38: validation accuracy 98.53%
Training mini-batch number 195000
Training mini-batch number 196000
Training mini-batch number 197000
Training mini-batch number 198000
Training mini-batch number 199000
Epoch 39: validation accuracy 98.54%
Training mini-batch number 200000
Training mini-batch number 201000
Training mini-batch number 202000
Training mini-batch number 203000
Training mini-batch number 204000
Epoch 40: validation accuracy 98.53%
Training mini-batch number 205000
Training mini-batch number 206000
Training mini-batch number 207000
Training mini-batch number 208000
Training mini-batch number 209000
Epoch 41: validation accuracy 98.53%
Training mini-batch number 210000
Training mini-batch number 211000
Training mini-batch number 212000
Training mini-batch number 213000
Training mini-batch number 214000
Epoch 42: validation accuracy 98.54%
Training mini-batch number 215000
Training mini-batch number 216000
Training mini-batch number 217000
Training mini-batch number 218000
Training mini-batch number 219000
Epoch 43: validation accuracy 98.54%
Training mini-batch number 220000
Training mini-batch number 221000
Training mini-batch number 222000
Training mini-batch number 223000
Training mini-batch number 224000
Epoch 44: validation accuracy 98.55%
Training mini-batch number 225000
Training mini-batch number 226000
Training mini-batch number 227000
Training mini-batch number 228000
Training mini-batch number 229000
Epoch 45: validation accuracy 98.52%
Training mini-batch number 230000
Training mini-batch number 231000
Training mini-batch number 232000
Training mini-batch number 233000
Training mini-batch number 234000
Epoch 46: validation accuracy 98.53%
Training mini-batch number 235000
Training mini-batch number 236000
Training mini-batch number 237000
Training mini-batch number 238000
Training mini-batch number 239000
Epoch 47: validation accuracy 98.51%
Training mini-batch number 240000
Training mini-batch number 241000
Training mini-batch number 242000
Training mini-batch number 243000
Training mini-batch number 244000
Epoch 48: validation accuracy 98.51%
Training mini-batch number 245000
Training mini-batch number 246000
Training mini-batch number 247000
Training mini-batch number 248000
Training mini-batch number 249000
Epoch 49: validation accuracy 98.51%
Training mini-batch number 250000
Training mini-batch number 251000
Training mini-batch number 252000
Training mini-batch number 253000
Training mini-batch number 254000
Epoch 50: validation accuracy 98.52%
Training mini-batch number 255000
Training mini-batch number 256000
Training mini-batch number 257000
Training mini-batch number 258000
Training mini-batch number 259000
Epoch 51: validation accuracy 98.51%
Training mini-batch number 260000
Training mini-batch number 261000
Training mini-batch number 262000
Training mini-batch number 263000
Training mini-batch number 264000
Epoch 52: validation accuracy 98.52%
Training mini-batch number 265000
Training mini-batch number 266000
Training mini-batch number 267000
Training mini-batch number 268000
Training mini-batch number 269000
Epoch 53: validation accuracy 98.52%
Training mini-batch number 270000
Training mini-batch number 271000
Training mini-batch number 272000
Training mini-batch number 273000
Training mini-batch number 274000
Epoch 54: validation accuracy 98.53%
Training mini-batch number 275000
Training mini-batch number 276000
Training mini-batch number 277000
Training mini-batch number 278000
Training mini-batch number 279000
Epoch 55: validation accuracy 98.52%
Training mini-batch number 280000
Training mini-batch number 281000
Training mini-batch number 282000
Training mini-batch number 283000
Training mini-batch number 284000
Epoch 56: validation accuracy 98.53%
Training mini-batch number 285000
Training mini-batch number 286000
Training mini-batch number 287000
Training mini-batch number 288000
Training mini-batch number 289000
Epoch 57: validation accuracy 98.53%
Training mini-batch number 290000
Training mini-batch number 291000
Training mini-batch number 292000
Training mini-batch number 293000
Training mini-batch number 294000
Epoch 58: validation accuracy 98.51%
Training mini-batch number 295000
Training mini-batch number 296000
Training mini-batch number 297000
Training mini-batch number 298000
Training mini-batch number 299000
Epoch 59: validation accuracy 98.51%
Finished training network.
Best validation accuracy of 98.61% obtained at iteration 109999
Corresponding test accuracy of 98.47%

















In [19]:



    


# add a second convolutional layer: 
net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
                     filter_shape=(20, 1, 5, 5),
                     poolsize=(2, 2)),
        ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),
                     filter_shape=(40, 20, 5, 5),
                     poolsize=(2, 2)),
        FullyConnectedLayer(n_in=40*4*4, n_out=100),
        SoftmaxLayer(n_in=100, n_out=10)
    ], mini_batch_size)



    











In [20]:



    


net.SGD(training_data, 60, mini_batch_size, 0.1, validation_data, test_data)



    


















    






Training mini-batch number 0
Training mini-batch number 1000
Training mini-batch number 2000
Training mini-batch number 3000
Training mini-batch number 4000
Epoch 0: validation accuracy 92.67%
This is the best validation accuracy to date.
The corresponding test accuracy is 92.43%
Training mini-batch number 5000
Training mini-batch number 6000
Training mini-batch number 7000
Training mini-batch number 8000
Training mini-batch number 9000
Epoch 1: validation accuracy 96.80%
This is the best validation accuracy to date.
The corresponding test accuracy is 96.53%
Training mini-batch number 10000
Training mini-batch number 11000
Training mini-batch number 12000
Training mini-batch number 13000
Training mini-batch number 14000
Epoch 2: validation accuracy 97.63%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.43%
Training mini-batch number 15000
Training mini-batch number 16000
Training mini-batch number 17000
Training mini-batch number 18000
Training mini-batch number 19000
Epoch 3: validation accuracy 97.89%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.74%
Training mini-batch number 20000
Training mini-batch number 21000
Training mini-batch number 22000
Training mini-batch number 23000
Training mini-batch number 24000
Epoch 4: validation accuracy 98.00%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.92%
Training mini-batch number 25000
Training mini-batch number 26000
Training mini-batch number 27000
Training mini-batch number 28000
Training mini-batch number 29000
Epoch 5: validation accuracy 98.15%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.14%
Training mini-batch number 30000
Training mini-batch number 31000
Training mini-batch number 32000
Training mini-batch number 33000
Training mini-batch number 34000
Epoch 6: validation accuracy 98.21%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.31%
Training mini-batch number 35000
Training mini-batch number 36000
Training mini-batch number 37000
Training mini-batch number 38000
Training mini-batch number 39000
Epoch 7: validation accuracy 98.27%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.44%
Training mini-batch number 40000
Training mini-batch number 41000
Training mini-batch number 42000
Training mini-batch number 43000
Training mini-batch number 44000
Epoch 8: validation accuracy 98.32%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.56%
Training mini-batch number 45000
Training mini-batch number 46000
Training mini-batch number 47000
Training mini-batch number 48000
Training mini-batch number 49000
Epoch 9: validation accuracy 98.39%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.58%
Training mini-batch number 50000
Training mini-batch number 51000
Training mini-batch number 52000
Training mini-batch number 53000
Training mini-batch number 54000
Epoch 10: validation accuracy 98.48%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.66%
Training mini-batch number 55000
Training mini-batch number 56000
Training mini-batch number 57000
Training mini-batch number 58000
Training mini-batch number 59000
Epoch 11: validation accuracy 98.55%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.70%
Training mini-batch number 60000
Training mini-batch number 61000
Training mini-batch number 62000
Training mini-batch number 63000
Training mini-batch number 64000
Epoch 12: validation accuracy 98.59%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.77%
Training mini-batch number 65000
Training mini-batch number 66000
Training mini-batch number 67000
Training mini-batch number 68000
Training mini-batch number 69000
Epoch 13: validation accuracy 98.65%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.80%
Training mini-batch number 70000
Training mini-batch number 71000
Training mini-batch number 72000
Training mini-batch number 73000
Training mini-batch number 74000
Epoch 14: validation accuracy 98.65%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.81%
Training mini-batch number 75000
Training mini-batch number 76000
Training mini-batch number 77000
Training mini-batch number 78000
Training mini-batch number 79000
Epoch 15: validation accuracy 98.70%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.86%
Training mini-batch number 80000
Training mini-batch number 81000
Training mini-batch number 82000
Training mini-batch number 83000
Training mini-batch number 84000
Epoch 16: validation accuracy 98.75%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.87%
Training mini-batch number 85000
Training mini-batch number 86000
Training mini-batch number 87000
Training mini-batch number 88000
Training mini-batch number 89000
Epoch 17: validation accuracy 98.77%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.88%
Training mini-batch number 90000
Training mini-batch number 91000
Training mini-batch number 92000
Training mini-batch number 93000
Training mini-batch number 94000
Epoch 18: validation accuracy 98.82%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.86%
Training mini-batch number 95000
Training mini-batch number 96000
Training mini-batch number 97000
Training mini-batch number 98000
Training mini-batch number 99000
Epoch 19: validation accuracy 98.84%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.88%
Training mini-batch number 100000
Training mini-batch number 101000
Training mini-batch number 102000
Training mini-batch number 103000
Training mini-batch number 104000
Epoch 20: validation accuracy 98.85%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.91%
Training mini-batch number 105000
Training mini-batch number 106000
Training mini-batch number 107000
Training mini-batch number 108000
Training mini-batch number 109000
Epoch 21: validation accuracy 98.84%
Training mini-batch number 110000
Training mini-batch number 111000
Training mini-batch number 112000
Training mini-batch number 113000
Training mini-batch number 114000
Epoch 22: validation accuracy 98.85%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.93%
Training mini-batch number 115000
Training mini-batch number 116000
Training mini-batch number 117000
Training mini-batch number 118000
Training mini-batch number 119000
Epoch 23: validation accuracy 98.86%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.95%
Training mini-batch number 120000
Training mini-batch number 121000
Training mini-batch number 122000
Training mini-batch number 123000
Training mini-batch number 124000
Epoch 24: validation accuracy 98.85%
Training mini-batch number 125000
Training mini-batch number 126000
Training mini-batch number 127000
Training mini-batch number 128000
Training mini-batch number 129000
Epoch 25: validation accuracy 98.86%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.98%
Training mini-batch number 130000
Training mini-batch number 131000
Training mini-batch number 132000
Training mini-batch number 133000
Training mini-batch number 134000
Epoch 26: validation accuracy 98.85%
Training mini-batch number 135000
Training mini-batch number 136000
Training mini-batch number 137000
Training mini-batch number 138000
Training mini-batch number 139000
Epoch 27: validation accuracy 98.84%
Training mini-batch number 140000
Training mini-batch number 141000
Training mini-batch number 142000
Training mini-batch number 143000
Training mini-batch number 144000
Epoch 28: validation accuracy 98.84%
Training mini-batch number 145000
Training mini-batch number 146000
Training mini-batch number 147000
Training mini-batch number 148000
Training mini-batch number 149000
Epoch 29: validation accuracy 98.85%
Training mini-batch number 150000
Training mini-batch number 151000
Training mini-batch number 152000
Training mini-batch number 153000
Training mini-batch number 154000
Epoch 30: validation accuracy 98.87%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.04%
Training mini-batch number 155000
Training mini-batch number 156000
Training mini-batch number 157000
Training mini-batch number 158000
Training mini-batch number 159000
Epoch 31: validation accuracy 98.87%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.03%
Training mini-batch number 160000
Training mini-batch number 161000
Training mini-batch number 162000
Training mini-batch number 163000
Training mini-batch number 164000
Epoch 32: validation accuracy 98.88%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.03%
Training mini-batch number 165000
Training mini-batch number 166000
Training mini-batch number 167000
Training mini-batch number 168000
Training mini-batch number 169000
Epoch 33: validation accuracy 98.89%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.05%
Training mini-batch number 170000
Training mini-batch number 171000
Training mini-batch number 172000
Training mini-batch number 173000
Training mini-batch number 174000
Epoch 34: validation accuracy 98.91%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.06%
Training mini-batch number 175000
Training mini-batch number 176000
Training mini-batch number 177000
Training mini-batch number 178000
Training mini-batch number 179000
Epoch 35: validation accuracy 98.92%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.06%
Training mini-batch number 180000
Training mini-batch number 181000
Training mini-batch number 182000
Training mini-batch number 183000
Training mini-batch number 184000
Epoch 36: validation accuracy 98.93%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.07%
Training mini-batch number 185000
Training mini-batch number 186000
Training mini-batch number 187000
Training mini-batch number 188000
Training mini-batch number 189000
Epoch 37: validation accuracy 98.94%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.06%
Training mini-batch number 190000
Training mini-batch number 191000
Training mini-batch number 192000
Training mini-batch number 193000
Training mini-batch number 194000
Epoch 38: validation accuracy 98.94%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.06%
Training mini-batch number 195000
Training mini-batch number 196000
Training mini-batch number 197000
Training mini-batch number 198000
Training mini-batch number 199000
Epoch 39: validation accuracy 98.95%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.05%
Training mini-batch number 200000
Training mini-batch number 201000
Training mini-batch number 202000
Training mini-batch number 203000
Training mini-batch number 204000
Epoch 40: validation accuracy 98.94%
Training mini-batch number 205000
Training mini-batch number 206000
Training mini-batch number 207000
Training mini-batch number 208000
Training mini-batch number 209000
Epoch 41: validation accuracy 98.92%
Training mini-batch number 210000
Training mini-batch number 211000
Training mini-batch number 212000
Training mini-batch number 213000
Training mini-batch number 214000
Epoch 42: validation accuracy 98.93%
Training mini-batch number 215000
Training mini-batch number 216000
Training mini-batch number 217000
Training mini-batch number 218000
Training mini-batch number 219000
Epoch 43: validation accuracy 98.93%
Training mini-batch number 220000
Training mini-batch number 221000
Training mini-batch number 222000
Training mini-batch number 223000
Training mini-batch number 224000
Epoch 44: validation accuracy 98.93%
Training mini-batch number 225000
Training mini-batch number 226000
Training mini-batch number 227000
Training mini-batch number 228000
Training mini-batch number 229000
Epoch 45: validation accuracy 98.95%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.08%
Training mini-batch number 230000
Training mini-batch number 231000
Training mini-batch number 232000
Training mini-batch number 233000
Training mini-batch number 234000
Epoch 46: validation accuracy 98.96%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.08%
Training mini-batch number 235000
Training mini-batch number 236000
Training mini-batch number 237000
Training mini-batch number 238000
Training mini-batch number 239000
Epoch 47: validation accuracy 98.95%
Training mini-batch number 240000
Training mini-batch number 241000
Training mini-batch number 242000
Training mini-batch number 243000
Training mini-batch number 244000
Epoch 48: validation accuracy 98.96%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.08%
Training mini-batch number 245000
Training mini-batch number 246000
Training mini-batch number 247000
Training mini-batch number 248000
Training mini-batch number 249000
Epoch 49: validation accuracy 98.95%
Training mini-batch number 250000
Training mini-batch number 251000
Training mini-batch number 252000
Training mini-batch number 253000
Training mini-batch number 254000
Epoch 50: validation accuracy 98.97%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.07%
Training mini-batch number 255000
Training mini-batch number 256000
Training mini-batch number 257000
Training mini-batch number 258000
Training mini-batch number 259000
Epoch 51: validation accuracy 98.98%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.07%
Training mini-batch number 260000
Training mini-batch number 261000
Training mini-batch number 262000
Training mini-batch number 263000
Training mini-batch number 264000
Epoch 52: validation accuracy 98.97%
Training mini-batch number 265000
Training mini-batch number 266000
Training mini-batch number 267000
Training mini-batch number 268000
Training mini-batch number 269000
Epoch 53: validation accuracy 98.97%
Training mini-batch number 270000
Training mini-batch number 271000
Training mini-batch number 272000
Training mini-batch number 273000
Training mini-batch number 274000
Epoch 54: validation accuracy 98.98%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.08%
Training mini-batch number 275000
Training mini-batch number 276000
Training mini-batch number 277000
Training mini-batch number 278000
Training mini-batch number 279000
Epoch 55: validation accuracy 98.98%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.08%
Training mini-batch number 280000
Training mini-batch number 281000
Training mini-batch number 282000
Training mini-batch number 283000
Training mini-batch number 284000
Epoch 56: validation accuracy 98.98%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.09%
Training mini-batch number 285000
Training mini-batch number 286000
Training mini-batch number 287000
Training mini-batch number 288000
Training mini-batch number 289000
Epoch 57: validation accuracy 98.98%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.10%
Training mini-batch number 290000
Training mini-batch number 291000
Training mini-batch number 292000
Training mini-batch number 293000
Training mini-batch number 294000
Epoch 58: validation accuracy 98.98%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.10%
Training mini-batch number 295000
Training mini-batch number 296000
Training mini-batch number 297000
Training mini-batch number 298000
Training mini-batch number 299000
Epoch 59: validation accuracy 98.98%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.11%
Finished training network.
Best validation accuracy of 98.98% obtained at iteration 299999
Corresponding test accuracy of 99.11%

















In [21]:



    


# switch from sigmoid to tanh neurons: 
net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
                     filter_shape=(20, 1, 5, 5),
                     poolsize=(2, 2),
                     activation_fn=tanh),
        ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),
                     filter_shape=(40, 20, 5, 5),
                     poolsize=(2, 2),
                     activation_fn=tanh),
        FullyConnectedLayer(n_in=40*4*4, n_out=100, activation_fn=tanh),
        SoftmaxLayer(n_in=100, n_out=10)
    ], mini_batch_size)



    











In [22]:



    


net.SGD(training_data, 60, mini_batch_size, 0.1, validation_data, test_data)



    


















    






Training mini-batch number 0
Training mini-batch number 1000
Training mini-batch number 2000
Training mini-batch number 3000
Training mini-batch number 4000
Epoch 0: validation accuracy 97.16%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.19%
Training mini-batch number 5000
Training mini-batch number 6000
Training mini-batch number 7000
Training mini-batch number 8000
Training mini-batch number 9000
Epoch 1: validation accuracy 98.10%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.03%
Training mini-batch number 10000
Training mini-batch number 11000
Training mini-batch number 12000
Training mini-batch number 13000
Training mini-batch number 14000
Epoch 2: validation accuracy 98.28%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.35%
Training mini-batch number 15000
Training mini-batch number 16000
Training mini-batch number 17000
Training mini-batch number 18000
Training mini-batch number 19000
Epoch 3: validation accuracy 98.32%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.14%
Training mini-batch number 20000
Training mini-batch number 21000
Training mini-batch number 22000
Training mini-batch number 23000
Training mini-batch number 24000
Epoch 4: validation accuracy 98.40%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.40%
Training mini-batch number 25000
Training mini-batch number 26000
Training mini-batch number 27000
Training mini-batch number 28000
Training mini-batch number 29000
Epoch 5: validation accuracy 98.68%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.55%
Training mini-batch number 30000
Training mini-batch number 31000
Training mini-batch number 32000
Training mini-batch number 33000
Training mini-batch number 34000
Epoch 6: validation accuracy 98.79%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.67%
Training mini-batch number 35000
Training mini-batch number 36000
Training mini-batch number 37000
Training mini-batch number 38000
Training mini-batch number 39000
Epoch 7: validation accuracy 98.77%
Training mini-batch number 40000
Training mini-batch number 41000
Training mini-batch number 42000
Training mini-batch number 43000
Training mini-batch number 44000
Epoch 8: validation accuracy 98.78%
Training mini-batch number 45000
Training mini-batch number 46000
Training mini-batch number 47000
Training mini-batch number 48000
Training mini-batch number 49000
Epoch 9: validation accuracy 98.92%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.97%
Training mini-batch number 50000
Training mini-batch number 51000
Training mini-batch number 52000
Training mini-batch number 53000
Training mini-batch number 54000
Epoch 10: validation accuracy 98.84%
Training mini-batch number 55000
Training mini-batch number 56000
Training mini-batch number 57000
Training mini-batch number 58000
Training mini-batch number 59000
Epoch 11: validation accuracy 98.88%
Training mini-batch number 60000
Training mini-batch number 61000
Training mini-batch number 62000
Training mini-batch number 63000
Training mini-batch number 64000
Epoch 12: validation accuracy 98.89%
Training mini-batch number 65000
Training mini-batch number 66000
Training mini-batch number 67000
Training mini-batch number 68000
Training mini-batch number 69000
Epoch 13: validation accuracy 98.87%
Training mini-batch number 70000
Training mini-batch number 71000
Training mini-batch number 72000
Training mini-batch number 73000
Training mini-batch number 74000
Epoch 14: validation accuracy 98.84%
Training mini-batch number 75000
Training mini-batch number 76000
Training mini-batch number 77000
Training mini-batch number 78000
Training mini-batch number 79000
Epoch 15: validation accuracy 98.85%
Training mini-batch number 80000
Training mini-batch number 81000
Training mini-batch number 82000
Training mini-batch number 83000
Training mini-batch number 84000
Epoch 16: validation accuracy 98.85%
Training mini-batch number 85000
Training mini-batch number 86000
Training mini-batch number 87000
Training mini-batch number 88000
Training mini-batch number 89000
Epoch 17: validation accuracy 98.84%
Training mini-batch number 90000
Training mini-batch number 91000
Training mini-batch number 92000
Training mini-batch number 93000
Training mini-batch number 94000
Epoch 18: validation accuracy 98.86%
Training mini-batch number 95000
Training mini-batch number 96000
Training mini-batch number 97000
Training mini-batch number 98000
Training mini-batch number 99000
Epoch 19: validation accuracy 98.86%
Training mini-batch number 100000
Training mini-batch number 101000
Training mini-batch number 102000
Training mini-batch number 103000
Training mini-batch number 104000
Epoch 20: validation accuracy 98.85%
Training mini-batch number 105000
Training mini-batch number 106000
Training mini-batch number 107000
Training mini-batch number 108000
Training mini-batch number 109000
Epoch 21: validation accuracy 98.85%
Training mini-batch number 110000
Training mini-batch number 111000
Training mini-batch number 112000
Training mini-batch number 113000
Training mini-batch number 114000
Epoch 22: validation accuracy 98.86%
Training mini-batch number 115000
Training mini-batch number 116000
Training mini-batch number 117000
Training mini-batch number 118000
Training mini-batch number 119000
Epoch 23: validation accuracy 98.86%
Training mini-batch number 120000
Training mini-batch number 121000
Training mini-batch number 122000
Training mini-batch number 123000
Training mini-batch number 124000
Epoch 24: validation accuracy 98.86%
Training mini-batch number 125000
Training mini-batch number 126000
Training mini-batch number 127000
Training mini-batch number 128000
Training mini-batch number 129000
Epoch 25: validation accuracy 98.85%
Training mini-batch number 130000
Training mini-batch number 131000
Training mini-batch number 132000
Training mini-batch number 133000
Training mini-batch number 134000
Epoch 26: validation accuracy 98.85%
Training mini-batch number 135000
Training mini-batch number 136000
Training mini-batch number 137000
Training mini-batch number 138000
Training mini-batch number 139000
Epoch 27: validation accuracy 98.85%
Training mini-batch number 140000
Training mini-batch number 141000
Training mini-batch number 142000
Training mini-batch number 143000
Training mini-batch number 144000
Epoch 28: validation accuracy 98.85%
Training mini-batch number 145000
Training mini-batch number 146000
Training mini-batch number 147000
Training mini-batch number 148000
Training mini-batch number 149000
Epoch 29: validation accuracy 98.85%
Training mini-batch number 150000
Training mini-batch number 151000
Training mini-batch number 152000
Training mini-batch number 153000
Training mini-batch number 154000
Epoch 30: validation accuracy 98.85%
Training mini-batch number 155000
Training mini-batch number 156000
Training mini-batch number 157000
Training mini-batch number 158000
Training mini-batch number 159000
Epoch 31: validation accuracy 98.87%
Training mini-batch number 160000
Training mini-batch number 161000
Training mini-batch number 162000
Training mini-batch number 163000
Training mini-batch number 164000
Epoch 32: validation accuracy 98.87%
Training mini-batch number 165000
Training mini-batch number 166000
Training mini-batch number 167000
Training mini-batch number 168000
Training mini-batch number 169000
Epoch 33: validation accuracy 98.88%
Training mini-batch number 170000
Training mini-batch number 171000
Training mini-batch number 172000
Training mini-batch number 173000
Training mini-batch number 174000
Epoch 34: validation accuracy 98.88%
Training mini-batch number 175000
Training mini-batch number 176000
Training mini-batch number 177000
Training mini-batch number 178000
Training mini-batch number 179000
Epoch 35: validation accuracy 98.88%
Training mini-batch number 180000
Training mini-batch number 181000
Training mini-batch number 182000
Training mini-batch number 183000
Training mini-batch number 184000
Epoch 36: validation accuracy 98.88%
Training mini-batch number 185000
Training mini-batch number 186000
Training mini-batch number 187000
Training mini-batch number 188000
Training mini-batch number 189000
Epoch 37: validation accuracy 98.89%
Training mini-batch number 190000
Training mini-batch number 191000
Training mini-batch number 192000
Training mini-batch number 193000
Training mini-batch number 194000
Epoch 38: validation accuracy 98.89%
Training mini-batch number 195000
Training mini-batch number 196000
Training mini-batch number 197000
Training mini-batch number 198000
Training mini-batch number 199000
Epoch 39: validation accuracy 98.89%
Training mini-batch number 200000
Training mini-batch number 201000
Training mini-batch number 202000
Training mini-batch number 203000
Training mini-batch number 204000
Epoch 40: validation accuracy 98.89%
Training mini-batch number 205000
Training mini-batch number 206000
Training mini-batch number 207000
Training mini-batch number 208000
Training mini-batch number 209000
Epoch 41: validation accuracy 98.89%
Training mini-batch number 210000
Training mini-batch number 211000
Training mini-batch number 212000
Training mini-batch number 213000
Training mini-batch number 214000
Epoch 42: validation accuracy 98.90%
Training mini-batch number 215000
Training mini-batch number 216000
Training mini-batch number 217000
Training mini-batch number 218000
Training mini-batch number 219000
Epoch 43: validation accuracy 98.89%
Training mini-batch number 220000
Training mini-batch number 221000
Training mini-batch number 222000
Training mini-batch number 223000
Training mini-batch number 224000
Epoch 44: validation accuracy 98.89%
Training mini-batch number 225000
Training mini-batch number 226000
Training mini-batch number 227000
Training mini-batch number 228000
Training mini-batch number 229000
Epoch 45: validation accuracy 98.89%
Training mini-batch number 230000
Training mini-batch number 231000
Training mini-batch number 232000
Training mini-batch number 233000
Training mini-batch number 234000
Epoch 46: validation accuracy 98.89%
Training mini-batch number 235000
Training mini-batch number 236000
Training mini-batch number 237000
Training mini-batch number 238000
Training mini-batch number 239000
Epoch 47: validation accuracy 98.89%
Training mini-batch number 240000
Training mini-batch number 241000
Training mini-batch number 242000
Training mini-batch number 243000
Training mini-batch number 244000
Epoch 48: validation accuracy 98.89%
Training mini-batch number 245000
Training mini-batch number 246000
Training mini-batch number 247000
Training mini-batch number 248000
Training mini-batch number 249000
Epoch 49: validation accuracy 98.88%
Training mini-batch number 250000
Training mini-batch number 251000
Training mini-batch number 252000
Training mini-batch number 253000
Training mini-batch number 254000
Epoch 50: validation accuracy 98.88%
Training mini-batch number 255000
Training mini-batch number 256000
Training mini-batch number 257000
Training mini-batch number 258000
Training mini-batch number 259000
Epoch 51: validation accuracy 98.88%
Training mini-batch number 260000
Training mini-batch number 261000
Training mini-batch number 262000
Training mini-batch number 263000
Training mini-batch number 264000
Epoch 52: validation accuracy 98.88%
Training mini-batch number 265000
Training mini-batch number 266000
Training mini-batch number 267000
Training mini-batch number 268000
Training mini-batch number 269000
Epoch 53: validation accuracy 98.88%
Training mini-batch number 270000
Training mini-batch number 271000
Training mini-batch number 272000
Training mini-batch number 273000
Training mini-batch number 274000
Epoch 54: validation accuracy 98.88%
Training mini-batch number 275000
Training mini-batch number 276000
Training mini-batch number 277000
Training mini-batch number 278000
Training mini-batch number 279000
Epoch 55: validation accuracy 98.88%
Training mini-batch number 280000
Training mini-batch number 281000
Training mini-batch number 282000
Training mini-batch number 283000
Training mini-batch number 284000
Epoch 56: validation accuracy 98.89%
Training mini-batch number 285000
Training mini-batch number 286000
Training mini-batch number 287000
Training mini-batch number 288000
Training mini-batch number 289000
Epoch 57: validation accuracy 98.89%
Training mini-batch number 290000
Training mini-batch number 291000
Training mini-batch number 292000
Training mini-batch number 293000
Training mini-batch number 294000
Epoch 58: validation accuracy 98.90%
Training mini-batch number 295000
Training mini-batch number 296000
Training mini-batch number 297000
Training mini-batch number 298000
Training mini-batch number 299000
Epoch 59: validation accuracy 98.90%
Finished training network.
Best validation accuracy of 98.92% obtained at iteration 49999
Corresponding test accuracy of 98.97%

















In [23]:



    


# switch to ReLU neurons: 
net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28), 
                      filter_shape=(20, 1, 5, 5), 
                      poolsize=(2, 2), 
                      activation_fn=ReLU),
        ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12), 
                      filter_shape=(40, 20, 5, 5), 
                      poolsize=(2, 2), 
                      activation_fn=ReLU),
        FullyConnectedLayer(n_in=40*4*4, n_out=100, activation_fn=ReLU),
        SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)



    











In [24]:



    


# note the eta is an order of magnitude smaller, and lambda has been added as a parameter: 
net.SGD(training_data, 60, mini_batch_size, 0.03, 
            validation_data, test_data, lmbda=0.1)



    


















    






Training mini-batch number 0
Training mini-batch number 1000
Training mini-batch number 2000
Training mini-batch number 3000
Training mini-batch number 4000
Epoch 0: validation accuracy 97.54%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.26%
Training mini-batch number 5000
Training mini-batch number 6000
Training mini-batch number 7000
Training mini-batch number 8000
Training mini-batch number 9000
Epoch 1: validation accuracy 98.05%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.00%
Training mini-batch number 10000
Training mini-batch number 11000
Training mini-batch number 12000
Training mini-batch number 13000
Training mini-batch number 14000
Epoch 2: validation accuracy 98.29%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.27%
Training mini-batch number 15000
Training mini-batch number 16000
Training mini-batch number 17000
Training mini-batch number 18000
Training mini-batch number 19000
Epoch 3: validation accuracy 98.57%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.57%
Training mini-batch number 20000
Training mini-batch number 21000
Training mini-batch number 22000
Training mini-batch number 23000
Training mini-batch number 24000
Epoch 4: validation accuracy 98.68%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.61%
Training mini-batch number 25000
Training mini-batch number 26000
Training mini-batch number 27000
Training mini-batch number 28000
Training mini-batch number 29000
Epoch 5: validation accuracy 98.82%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.77%
Training mini-batch number 30000
Training mini-batch number 31000
Training mini-batch number 32000
Training mini-batch number 33000
Training mini-batch number 34000
Epoch 6: validation accuracy 98.75%
Training mini-batch number 35000
Training mini-batch number 36000
Training mini-batch number 37000
Training mini-batch number 38000
Training mini-batch number 39000
Epoch 7: validation accuracy 98.71%
Training mini-batch number 40000
Training mini-batch number 41000
Training mini-batch number 42000
Training mini-batch number 43000
Training mini-batch number 44000
Epoch 8: validation accuracy 98.77%
Training mini-batch number 45000
Training mini-batch number 46000
Training mini-batch number 47000
Training mini-batch number 48000
Training mini-batch number 49000
Epoch 9: validation accuracy 98.90%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.76%
Training mini-batch number 50000
Training mini-batch number 51000
Training mini-batch number 52000
Training mini-batch number 53000
Training mini-batch number 54000
Epoch 10: validation accuracy 98.74%
Training mini-batch number 55000
Training mini-batch number 56000
Training mini-batch number 57000
Training mini-batch number 58000
Training mini-batch number 59000
Epoch 11: validation accuracy 98.74%
Training mini-batch number 60000
Training mini-batch number 61000
Training mini-batch number 62000
Training mini-batch number 63000
Training mini-batch number 64000
Epoch 12: validation accuracy 98.94%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.93%
Training mini-batch number 65000
Training mini-batch number 66000
Training mini-batch number 67000
Training mini-batch number 68000
Training mini-batch number 69000
Epoch 13: validation accuracy 98.93%
Training mini-batch number 70000
Training mini-batch number 71000
Training mini-batch number 72000
Training mini-batch number 73000
Training mini-batch number 74000
Epoch 14: validation accuracy 98.75%
Training mini-batch number 75000
Training mini-batch number 76000
Training mini-batch number 77000
Training mini-batch number 78000
Training mini-batch number 79000
Epoch 15: validation accuracy 98.77%
Training mini-batch number 80000
Training mini-batch number 81000
Training mini-batch number 82000
Training mini-batch number 83000
Training mini-batch number 84000
Epoch 16: validation accuracy 98.95%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.98%
Training mini-batch number 85000
Training mini-batch number 86000
Training mini-batch number 87000
Training mini-batch number 88000
Training mini-batch number 89000
Epoch 17: validation accuracy 98.91%
Training mini-batch number 90000
Training mini-batch number 91000
Training mini-batch number 92000
Training mini-batch number 93000
Training mini-batch number 94000
Epoch 18: validation accuracy 98.66%
Training mini-batch number 95000
Training mini-batch number 96000
Training mini-batch number 97000
Training mini-batch number 98000
Training mini-batch number 99000
Epoch 19: validation accuracy 98.80%
Training mini-batch number 100000
Training mini-batch number 101000
Training mini-batch number 102000
Training mini-batch number 103000
Training mini-batch number 104000
Epoch 20: validation accuracy 98.82%
Training mini-batch number 105000
Training mini-batch number 106000
Training mini-batch number 107000
Training mini-batch number 108000
Training mini-batch number 109000
Epoch 21: validation accuracy 98.84%
Training mini-batch number 110000
Training mini-batch number 111000
Training mini-batch number 112000
Training mini-batch number 113000
Training mini-batch number 114000
Epoch 22: validation accuracy 98.81%
Training mini-batch number 115000
Training mini-batch number 116000
Training mini-batch number 117000
Training mini-batch number 118000
Training mini-batch number 119000
Epoch 23: validation accuracy 99.01%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.00%
Training mini-batch number 120000
Training mini-batch number 121000
Training mini-batch number 122000
Training mini-batch number 123000
Training mini-batch number 124000
Epoch 24: validation accuracy 98.87%
Training mini-batch number 125000
Training mini-batch number 126000
Training mini-batch number 127000
Training mini-batch number 128000
Training mini-batch number 129000
Epoch 25: validation accuracy 99.04%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.99%
Training mini-batch number 130000
Training mini-batch number 131000
Training mini-batch number 132000
Training mini-batch number 133000
Training mini-batch number 134000
Epoch 26: validation accuracy 98.79%
Training mini-batch number 135000
Training mini-batch number 136000
Training mini-batch number 137000
Training mini-batch number 138000
Training mini-batch number 139000
Epoch 27: validation accuracy 99.02%
Training mini-batch number 140000
Training mini-batch number 141000
Training mini-batch number 142000
Training mini-batch number 143000
Training mini-batch number 144000
Epoch 28: validation accuracy 99.11%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.09%
Training mini-batch number 145000
Training mini-batch number 146000
Training mini-batch number 147000
Training mini-batch number 148000
Training mini-batch number 149000
Epoch 29: validation accuracy 99.20%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.09%
Training mini-batch number 150000
Training mini-batch number 151000
Training mini-batch number 152000
Training mini-batch number 153000
Training mini-batch number 154000
Epoch 30: validation accuracy 99.20%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.11%
Training mini-batch number 155000
Training mini-batch number 156000
Training mini-batch number 157000
Training mini-batch number 158000
Training mini-batch number 159000
Epoch 31: validation accuracy 99.19%
Training mini-batch number 160000
Training mini-batch number 161000
Training mini-batch number 162000
Training mini-batch number 163000
Training mini-batch number 164000
Epoch 32: validation accuracy 99.19%
Training mini-batch number 165000
Training mini-batch number 166000
Training mini-batch number 167000
Training mini-batch number 168000
Training mini-batch number 169000
Epoch 33: validation accuracy 99.19%
Training mini-batch number 170000
Training mini-batch number 171000
Training mini-batch number 172000
Training mini-batch number 173000
Training mini-batch number 174000
Epoch 34: validation accuracy 99.17%
Training mini-batch number 175000
Training mini-batch number 176000
Training mini-batch number 177000
Training mini-batch number 178000
Training mini-batch number 179000
Epoch 35: validation accuracy 99.18%
Training mini-batch number 180000
Training mini-batch number 181000
Training mini-batch number 182000
Training mini-batch number 183000
Training mini-batch number 184000
Epoch 36: validation accuracy 99.18%
Training mini-batch number 185000
Training mini-batch number 186000
Training mini-batch number 187000
Training mini-batch number 188000
Training mini-batch number 189000
Epoch 37: validation accuracy 99.18%
Training mini-batch number 190000
Training mini-batch number 191000
Training mini-batch number 192000
Training mini-batch number 193000
Training mini-batch number 194000
Epoch 38: validation accuracy 99.17%
Training mini-batch number 195000
Training mini-batch number 196000
Training mini-batch number 197000
Training mini-batch number 198000
Training mini-batch number 199000
Epoch 39: validation accuracy 99.17%
Training mini-batch number 200000
Training mini-batch number 201000
Training mini-batch number 202000
Training mini-batch number 203000
Training mini-batch number 204000
Epoch 40: validation accuracy 99.18%
Training mini-batch number 205000
Training mini-batch number 206000
Training mini-batch number 207000
Training mini-batch number 208000
Training mini-batch number 209000
Epoch 41: validation accuracy 99.17%
Training mini-batch number 210000
Training mini-batch number 211000
Training mini-batch number 212000
Training mini-batch number 213000
Training mini-batch number 214000
Epoch 42: validation accuracy 99.18%
Training mini-batch number 215000
Training mini-batch number 216000
Training mini-batch number 217000
Training mini-batch number 218000
Training mini-batch number 219000
Epoch 43: validation accuracy 99.16%
Training mini-batch number 220000
Training mini-batch number 221000
Training mini-batch number 222000
Training mini-batch number 223000
Training mini-batch number 224000
Epoch 44: validation accuracy 99.17%
Training mini-batch number 225000
Training mini-batch number 226000
Training mini-batch number 227000
Training mini-batch number 228000
Training mini-batch number 229000
Epoch 45: validation accuracy 99.15%
Training mini-batch number 230000
Training mini-batch number 231000
Training mini-batch number 232000
Training mini-batch number 233000
Training mini-batch number 234000
Epoch 46: validation accuracy 99.15%
Training mini-batch number 235000
Training mini-batch number 236000
Training mini-batch number 237000
Training mini-batch number 238000
Training mini-batch number 239000
Epoch 47: validation accuracy 99.15%
Training mini-batch number 240000
Training mini-batch number 241000
Training mini-batch number 242000
Training mini-batch number 243000
Training mini-batch number 244000
Epoch 48: validation accuracy 99.16%
Training mini-batch number 245000
Training mini-batch number 246000
Training mini-batch number 247000
Training mini-batch number 248000
Training mini-batch number 249000
Epoch 49: validation accuracy 99.17%
Training mini-batch number 250000
Training mini-batch number 251000
Training mini-batch number 252000
Training mini-batch number 253000
Training mini-batch number 254000
Epoch 50: validation accuracy 99.17%
Training mini-batch number 255000
Training mini-batch number 256000
Training mini-batch number 257000
Training mini-batch number 258000
Training mini-batch number 259000
Epoch 51: validation accuracy 99.17%
Training mini-batch number 260000
Training mini-batch number 261000
Training mini-batch number 262000
Training mini-batch number 263000
Training mini-batch number 264000
Epoch 52: validation accuracy 99.17%
Training mini-batch number 265000
Training mini-batch number 266000
Training mini-batch number 267000
Training mini-batch number 268000
Training mini-batch number 269000
Epoch 53: validation accuracy 99.17%
Training mini-batch number 270000
Training mini-batch number 271000
Training mini-batch number 272000
Training mini-batch number 273000
Training mini-batch number 274000
Epoch 54: validation accuracy 99.18%
Training mini-batch number 275000
Training mini-batch number 276000
Training mini-batch number 277000
Training mini-batch number 278000
Training mini-batch number 279000
Epoch 55: validation accuracy 99.18%
Training mini-batch number 280000
Training mini-batch number 281000
Training mini-batch number 282000
Training mini-batch number 283000
Training mini-batch number 284000
Epoch 56: validation accuracy 99.18%
Training mini-batch number 285000
Training mini-batch number 286000
Training mini-batch number 287000
Training mini-batch number 288000
Training mini-batch number 289000
Epoch 57: validation accuracy 99.18%
Training mini-batch number 290000
Training mini-batch number 291000
Training mini-batch number 292000
Training mini-batch number 293000
Training mini-batch number 294000
Epoch 58: validation accuracy 99.18%
Training mini-batch number 295000
Training mini-batch number 296000
Training mini-batch number 297000
Training mini-batch number 298000
Training mini-batch number 299000
Epoch 59: validation accuracy 99.18%
Finished training network.
Best validation accuracy of 99.20% obtained at iteration 154999
Corresponding test accuracy of 99.11%

















In [13]:



    


expanded_training_data, _, _ = load_data_shared("../../neural-networks-and-deep-learning/data/mnist_expanded.pkl.gz")



    











In [26]:



    


net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28), 
                      filter_shape=(20, 1, 5, 5), 
                      poolsize=(2, 2), 
                      activation_fn=ReLU),
        ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12), 
                      filter_shape=(40, 20, 5, 5), 
                      poolsize=(2, 2), 
                      activation_fn=ReLU),
        FullyConnectedLayer(n_in=40*4*4, n_out=100, activation_fn=ReLU),
        SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)



    











In [27]:



    


net.SGD(expanded_training_data, 60, mini_batch_size, 0.03, 
            validation_data, test_data, lmbda=0.1)



    


















    






Training mini-batch number 0
Training mini-batch number 1000
Training mini-batch number 2000
Training mini-batch number 3000
Training mini-batch number 4000
Training mini-batch number 5000
Training mini-batch number 6000
Training mini-batch number 7000
Training mini-batch number 8000
Training mini-batch number 9000
Training mini-batch number 10000
Training mini-batch number 11000
Training mini-batch number 12000
Training mini-batch number 13000
Training mini-batch number 14000
Training mini-batch number 15000
Training mini-batch number 16000
Training mini-batch number 17000
Training mini-batch number 18000
Training mini-batch number 19000
Training mini-batch number 20000
Training mini-batch number 21000
Training mini-batch number 22000
Training mini-batch number 23000
Training mini-batch number 24000
Epoch 0: validation accuracy 96.92%
This is the best validation accuracy to date.
The corresponding test accuracy is 97.05%
Training mini-batch number 25000
Training mini-batch number 26000
Training mini-batch number 27000
Training mini-batch number 28000
Training mini-batch number 29000
Training mini-batch number 30000
Training mini-batch number 31000
Training mini-batch number 32000
Training mini-batch number 33000
Training mini-batch number 34000
Training mini-batch number 35000
Training mini-batch number 36000
Training mini-batch number 37000
Training mini-batch number 38000
Training mini-batch number 39000
Training mini-batch number 40000
Training mini-batch number 41000
Training mini-batch number 42000
Training mini-batch number 43000
Training mini-batch number 44000
Training mini-batch number 45000
Training mini-batch number 46000
Training mini-batch number 47000
Training mini-batch number 48000
Training mini-batch number 49000
Epoch 1: validation accuracy 98.79%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.87%
Training mini-batch number 50000
Training mini-batch number 51000
Training mini-batch number 52000
Training mini-batch number 53000
Training mini-batch number 54000
Training mini-batch number 55000
Training mini-batch number 56000
Training mini-batch number 57000
Training mini-batch number 58000
Training mini-batch number 59000
Training mini-batch number 60000
Training mini-batch number 61000
Training mini-batch number 62000
Training mini-batch number 63000
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Training mini-batch number 68000
Training mini-batch number 69000
Training mini-batch number 70000
Training mini-batch number 71000
Training mini-batch number 72000
Training mini-batch number 73000
Training mini-batch number 74000
Epoch 2: validation accuracy 98.99%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.88%
Training mini-batch number 75000
Training mini-batch number 76000
Training mini-batch number 77000
Training mini-batch number 78000
Training mini-batch number 79000
Training mini-batch number 80000
Training mini-batch number 81000
Training mini-batch number 82000
Training mini-batch number 83000
Training mini-batch number 84000
Training mini-batch number 85000
Training mini-batch number 86000
Training mini-batch number 87000
Training mini-batch number 88000
Training mini-batch number 89000
Training mini-batch number 90000
Training mini-batch number 91000
Training mini-batch number 92000
Training mini-batch number 93000
Training mini-batch number 94000
Training mini-batch number 95000
Training mini-batch number 96000
Training mini-batch number 97000
Training mini-batch number 98000
Training mini-batch number 99000
Epoch 3: validation accuracy 99.11%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.93%
Training mini-batch number 100000
Training mini-batch number 101000
Training mini-batch number 102000
Training mini-batch number 103000
Training mini-batch number 104000
Training mini-batch number 105000
Training mini-batch number 106000
Training mini-batch number 107000
Training mini-batch number 108000
Training mini-batch number 109000
Training mini-batch number 110000
Training mini-batch number 111000
Training mini-batch number 112000
Training mini-batch number 113000
Training mini-batch number 114000
Training mini-batch number 115000
Training mini-batch number 116000
Training mini-batch number 117000
Training mini-batch number 118000
Training mini-batch number 119000
Training mini-batch number 120000
Training mini-batch number 121000
Training mini-batch number 122000
Training mini-batch number 123000
Training mini-batch number 124000
Epoch 4: validation accuracy 99.07%
Training mini-batch number 125000
Training mini-batch number 126000
Training mini-batch number 127000
Training mini-batch number 128000
Training mini-batch number 129000
Training mini-batch number 130000
Training mini-batch number 131000
Training mini-batch number 132000
Training mini-batch number 133000
Training mini-batch number 134000
Training mini-batch number 135000
Training mini-batch number 136000
Training mini-batch number 137000
Training mini-batch number 138000
Training mini-batch number 139000
Training mini-batch number 140000
Training mini-batch number 141000
Training mini-batch number 142000
Training mini-batch number 143000
Training mini-batch number 144000
Training mini-batch number 145000
Training mini-batch number 146000
Training mini-batch number 147000
Training mini-batch number 148000
Training mini-batch number 149000
Epoch 5: validation accuracy 99.12%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.98%
Training mini-batch number 150000
Training mini-batch number 151000
Training mini-batch number 152000
Training mini-batch number 153000
Training mini-batch number 154000
Training mini-batch number 155000
Training mini-batch number 156000
Training mini-batch number 157000
Training mini-batch number 158000
Training mini-batch number 159000
Training mini-batch number 160000
Training mini-batch number 161000
Training mini-batch number 162000
Training mini-batch number 163000
Training mini-batch number 164000
Training mini-batch number 165000
Training mini-batch number 166000
Training mini-batch number 167000
Training mini-batch number 168000
Training mini-batch number 169000
Training mini-batch number 170000
Training mini-batch number 171000
Training mini-batch number 172000
Training mini-batch number 173000
Training mini-batch number 174000
Epoch 6: validation accuracy 99.16%
This is the best validation accuracy to date.
The corresponding test accuracy is 98.94%
Training mini-batch number 175000
Training mini-batch number 176000
Training mini-batch number 177000
Training mini-batch number 178000
Training mini-batch number 179000
Training mini-batch number 180000
Training mini-batch number 181000
Training mini-batch number 182000
Training mini-batch number 183000
Training mini-batch number 184000
Training mini-batch number 185000
Training mini-batch number 186000
Training mini-batch number 187000
Training mini-batch number 188000
Training mini-batch number 189000
Training mini-batch number 190000
Training mini-batch number 191000
Training mini-batch number 192000
Training mini-batch number 193000
Training mini-batch number 194000
Training mini-batch number 195000
Training mini-batch number 196000
Training mini-batch number 197000
Training mini-batch number 198000
Training mini-batch number 199000
Epoch 7: validation accuracy 99.16%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.06%
Training mini-batch number 200000
Training mini-batch number 201000
Training mini-batch number 202000
Training mini-batch number 203000
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Epoch 8: validation accuracy 99.27%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.19%
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Epoch 9: validation accuracy 99.31%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.17%
Training mini-batch number 250000
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Epoch 10: validation accuracy 99.26%
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Epoch 11: validation accuracy 99.34%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.24%
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Epoch 12: validation accuracy 99.17%
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Epoch 13: validation accuracy 99.23%
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Epoch 14: validation accuracy 99.15%
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Epoch 15: validation accuracy 99.26%
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Epoch 16: validation accuracy 99.35%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.28%
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Epoch 17: validation accuracy 99.30%
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Epoch 18: validation accuracy 99.31%
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Epoch 19: validation accuracy 99.40%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.33%
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Epoch 20: validation accuracy 99.33%
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Epoch 21: validation accuracy 99.27%
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Epoch 22: validation accuracy 99.40%
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Epoch 23: validation accuracy 99.37%
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Epoch 24: validation accuracy 99.38%
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Epoch 25: validation accuracy 99.27%
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Epoch 26: validation accuracy 99.25%
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Epoch 27: validation accuracy 99.30%
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Epoch 28: validation accuracy 99.30%
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Epoch 29: validation accuracy 99.33%
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Epoch 30: validation accuracy 99.36%
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Epoch 31: validation accuracy 99.38%
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Epoch 32: validation accuracy 99.38%
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Epoch 33: validation accuracy 99.37%
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Epoch 34: validation accuracy 99.38%
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Epoch 35: validation accuracy 99.37%
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Epoch 36: validation accuracy 99.38%
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Epoch 37: validation accuracy 99.39%
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Epoch 38: validation accuracy 99.38%
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Epoch 39: validation accuracy 99.38%
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Epoch 59: validation accuracy 99.38%
Finished training network.
Best validation accuracy of 99.40% obtained at iteration 499999
Corresponding test accuracy of 99.33%

















In [14]:



    


net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),
                     filter_shape=(20, 1, 5, 5),
                     poolsize=(2, 2),
                     activation_fn=ReLU),
        ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),
                     filter_shape=(40, 20, 5, 5),
                     poolsize=(2, 2),
                     activation_fn=ReLU),
        FullyConnectedLayer(
            n_in=40*4*4, n_out=1000, activation_fn=ReLU, p_dropout=0.5),
        FullyConnectedLayer(
            n_in=1000, n_out=1000, activation_fn=ReLU, p_dropout=0.5),
        SoftmaxLayer(n_in=1000, n_out=10, p_dropout=0.5)],
        mini_batch_size)



    











In [15]:



    


net.SGD(expanded_training_data, 40, mini_batch_size, 0.03, validation_data, test_data)



    


















    






ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(Rebroadcast{?,0}.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Rebroadcast{?,0}.0) of Op Elemwise{add,no_inplace}(Rebroadcast{?,0}.0, TensorConstant{(1, 1, 1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(Subtensor{::, ::, ::int64, ::int64}.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Subtensor{::, ::, ::int64, ::int64}.0) of Op Elemwise{add,no_inplace}(Subtensor{::, ::, ::int64, ::int64}.0, TensorConstant{(1, 1, 1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(Elemwise{add,no_inplace}.0, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Elemwise{add,no_inplace}.0) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, Elemwise{add,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_canonizer
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{1})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{add,no_inplace}(TensorConstant{1}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(Rebroadcast{?,0}.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Rebroadcast{?,0}.0) of Op Elemwise{add,no_inplace}(Rebroadcast{?,0}.0, TensorConstant{(1, 1, 1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(Subtensor{::, ::, ::int64, ::int64}.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Subtensor{::, ::, ::int64, ::int64}.0) of Op Elemwise{add,no_inplace}(Subtensor{::, ::, ::int64, ::int64}.0, TensorConstant{(1, 1, 1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(Elemwise{add,no_inplace}.0, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Elemwise{add,no_inplace}.0) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, Elemwise{add,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_canonizer
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{1})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{add,no_inplace}(TensorConstant{1}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(Rebroadcast{?,0}.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Rebroadcast{?,0}.0) of Op Elemwise{add,no_inplace}(Rebroadcast{?,0}.0, TensorConstant{(1, 1, 1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(Subtensor{::, ::, ::int64, ::int64}.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Subtensor{::, ::, ::int64, ::int64}.0) of Op Elemwise{add,no_inplace}(Subtensor{::, ::, ::int64, ::int64}.0, TensorConstant{(1, 1, 1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_sink
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1621, in local_fill_sink
    c = node.op(*inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{add,no_inplace}(dot.0, TensorConstant{(1, 1) of 0.0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(Elemwise{add,no_inplace}.0, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Elemwise{add,no_inplace}.0) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, Elemwise{add,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_canonizer
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{10})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{mul,no_inplace}(TensorConstant{10}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_canonizer
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(<TensorType(int64, scalar)>, TensorConstant{1})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4505, in transform
    new = self.merge_num_denum(num, denum)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 4326, in merge_num_denum
    return self.main(*num)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<TensorType(int64, scalar)>) of Op Elemwise{add,no_inplace}(TensorConstant{1}, <TensorType(int64, scalar)>) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second}(AdvancedSubtensor.0, Elemwise{true_div}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{true_div}.0) of Op Alloc(Elemwise{true_div}.0, Elemwise{sub,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(Elemwise{log,no_inplace}.0, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 2 (Shape_i{1}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, TensorConstant{10}, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(<TensorType(float64, 4D)>, TensorConstant{(1, 1, 1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1, 1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0, Shape_i{2}.0, Shape_i{3}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(<TensorType(float64, 4D)>, TensorConstant{(1, 1, 1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1, 1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0, Shape_i{2}.0, Shape_i{3}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(<TensorType(float64, 4D)>, TensorConstant{(1, 1, 1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1, 1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0, Shape_i{2}.0, Shape_i{3}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(<TensorType(float64, 4D)>, TensorConstant{(1, 1, 1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1, 1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0, Shape_i{2}.0, Shape_i{3}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(Elemwise{log,no_inplace}.0, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 2 (Shape_i{1}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, TensorConstant{10}, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second}(AdvancedSubtensor.0, Elemwise{true_div}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{true_div}.0) of Op Alloc(Elemwise{true_div}.0, Elemwise{sub,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(Elemwise{mul,no_inplace}.0, w)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op Dot22(Elemwise{mul,no_inplace}.0, w) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(Elemwise{mul,no_inplace}.0, w)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op Dot22(Elemwise{mul,no_inplace}.0, w) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(Elemwise{mul,no_inplace}.0, w)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op Dot22(Elemwise{mul,no_inplace}.0, w) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(DimShuffle{1,0}.0, SoftmaxGrad.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (DimShuffle{1,0}.0) of Op Dot22(DimShuffle{1,0}.0, SoftmaxGrad.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(SoftmaxGrad.0, w.T)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (SoftmaxGrad.0) of Op Dot22(SoftmaxGrad.0, w.T) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(DimShuffle{1,0}.0, Elemwise{mul}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (DimShuffle{1,0}.0) of Op Dot22(DimShuffle{1,0}.0, Elemwise{mul}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(Elemwise{mul}.0, w.T)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul}.0) of Op Dot22(Elemwise{mul}.0, w.T) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(DimShuffle{1,0}.0, Elemwise{mul}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (DimShuffle{1,0}.0) of Op Dot22(DimShuffle{1,0}.0, Elemwise{mul}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_dot_to_dot22
ERROR (theano.gof.opt): node: dot(Elemwise{mul}.0, w.T)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/blas.py", line 1633, in local_dot_to_dot22
    return [_dot22(*node.inputs)]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul}.0) of Op Dot22(Elemwise{mul}.0, w.T) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_specialize
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(Rebroadcast{?,0}.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5485, in local_add_specialize
    ret = fill_chain(new_inputs[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5458, in fill_chain
    out = _fill_chain(v, node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 142, in _fill_chain
    new_out = T.fill(i, new_out)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Rebroadcast{?,0}.0) of Op Elemwise{second,no_inplace}(Rebroadcast{?,0}.0, Rebroadcast{?,0}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_specialize
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(Subtensor{::, ::, ::int64, ::int64}.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5485, in local_add_specialize
    ret = fill_chain(new_inputs[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5458, in fill_chain
    out = _fill_chain(v, node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 142, in _fill_chain
    new_out = T.fill(i, new_out)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Subtensor{::, ::, ::int64, ::int64}.0) of Op Elemwise{second,no_inplace}(Subtensor{::, ::, ::int64, ::int64}.0, Subtensor{::, ::, ::int64, ::int64}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_specialize
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5485, in local_add_specialize
    ret = fill_chain(new_inputs[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5458, in fill_chain
    out = _fill_chain(v, node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 142, in _fill_chain
    new_out = T.fill(i, new_out)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{second,no_inplace}(dot.0, dot.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_specialize
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5485, in local_add_specialize
    ret = fill_chain(new_inputs[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5458, in fill_chain
    out = _fill_chain(v, node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 142, in _fill_chain
    new_out = T.fill(i, new_out)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{second,no_inplace}(dot.0, dot.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_add_specialize
ERROR (theano.gof.opt): node: Elemwise{add,no_inplace}(dot.0, Elemwise{second,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5485, in local_add_specialize
    ret = fill_chain(new_inputs[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5458, in fill_chain
    out = _fill_chain(v, node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 142, in _fill_chain
    new_out = T.fill(i, new_out)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (dot.0) of Op Elemwise{second,no_inplace}(dot.0, dot.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_advanced_indexing_crossentropy_onehot_grad
ERROR (theano.gof.opt): node: SoftmaxGrad(Elemwise{true_div}.0, Softmax.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/nnet/nnet.py", line 1907, in local_advanced_indexing_crossentropy_onehot_grad
    out_grad = -incr
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/var.py", line 39, in __neg__
    return theano.tensor.basic.neg(self)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{second}.0) of Op Elemwise{neg,no_inplace}(Elemwise{second}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_mul_specialize
ERROR (theano.gof.opt): node: Elemwise{mul,no_inplace}(TensorConstant{-1.0}, mean)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 5427, in local_mul_specialize
    rval = -new_inputs[0]
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/var.py", line 39, in __neg__
    return theano.tensor.basic.neg(self)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (mean) of Op Elemwise{neg,no_inplace}(mean) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second}(AdvancedSubtensor.0, Elemwise{true_div}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{true_div}.0) of Op Alloc(Elemwise{true_div}.0, Elemwise{sub,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(Elemwise{log,no_inplace}.0, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 2 (Shape_i{1}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, TensorConstant{10}, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_advanced_indexing_crossentropy_onehot
ERROR (theano.gof.opt): node: AdvancedSubtensor(Elemwise{log,no_inplace}.0, ARange{dtype='int64'}.0, Elemwise{Cast{int32}}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/nnet/nnet.py", line 1692, in local_advanced_indexing_crossentropy_onehot
    b_var = tensor.zeros_like(x_var[0])
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/var.py", line 532, in __getitem__
    lambda entry: isinstance(entry, Variable)))
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{add,no_inplace}.0) of Op Subtensor{int64}(Elemwise{add,no_inplace}.0, Constant{0}) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_logsoftmax
ERROR (theano.gof.opt): node: Elemwise{log,no_inplace}(Softmax.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/nnet/nnet.py", line 756, in local_logsoftmax
    ret = new_op(inVars)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{add,no_inplace}.0) of Op LogSoftmax(Elemwise{add,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(w, TensorConstant{(1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(<TensorType(float64, 4D)>, TensorConstant{(1, 1, 1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1, 1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0, Shape_i{2}.0, Shape_i{3}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: local_fill_to_alloc
ERROR (theano.gof.opt): node: Elemwise{second,no_inplace}(<TensorType(float64, 4D)>, TensorConstant{(1, 1, 1, 1) of 0.0})
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 1669, in local_fill_to_alloc
    o = broadcast_like(v, r, node.fgraph, dtype=v.dtype)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 208, in broadcast_like
    rval = T.alloc(T.cast(value, dtype), *new_shape)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/basic.py", line 2810, in __call__
    ret = super(Alloc, self).__call__(val, *shapes, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (Shape_i{0}.0) of Op Alloc(TensorConstant{(1, 1, 1, 1) of 0.0}, Shape_i{0}.0, Shape_i{1}.0, Shape_i{2}.0, Shape_i{3}.0) missing default value

ERROR (theano.gof.opt): SeqOptimizer apply <theano.tensor.opt.FusionOptimizer object at 0x7f862ca00ad0>
ERROR (theano.gof.opt): Traceback:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 230, in apply
    sub_prof = optimizer.optimize(fgraph)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 89, in optimize
    ret = self.apply(fgraph, *args, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 6658, in apply
    new_outputs = self.optimizer(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 6502, in local_fuse
    return_list=True)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 1 (<float64>) of Op mul(<float64>, <float64>) missing default value

ERROR (theano.gof.opt): SeqOptimizer apply <theano.compile.mode.AddDestroyHandler object at 0x7f8649eada50>
ERROR (theano.gof.opt): Traceback:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 230, in apply
    sub_prof = optimizer.optimize(fgraph)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 89, in optimize
    ret = self.apply(fgraph, *args, **kwargs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/compile/mode.py", line 134, in apply
    fgraph.replace_validate(o, _output_guard(o),
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op OutputGuard(Elemwise{mul,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0,2,3}(Reshape{4}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Reshape{4}.0) of Op InplaceDimShuffle{1,0,2,3}(Reshape{4}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0,2,3}(Reshape{4}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Reshape{4}.0) of Op InplaceDimShuffle{1,0,2,3}(Reshape{4}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{x}(Elemwise{Cast{float64}}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{Cast{float64}}.0) of Op InplaceDimShuffle{x}(Elemwise{Cast{float64}}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0}(Elemwise{mul,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op InplaceDimShuffle{1,0}(Elemwise{mul,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0}(Elemwise{mul,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op InplaceDimShuffle{1,0}(Elemwise{mul,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0}(Elemwise{mul,no_inplace}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (Elemwise{mul,no_inplace}.0) of Op InplaceDimShuffle{1,0}(Elemwise{mul,no_inplace}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0,2,3}(MaxPoolGrad{ds=(2, 2), ignore_border=True, st=(2, 2), padding=(0, 0), mode='max'}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (MaxPoolGrad{ds=(2, 2), ignore_border=True, st=(2, 2), padding=(0, 0), mode='max'}.0) of Op InplaceDimShuffle{1,0,2,3}(MaxPoolGrad{ds=(2, 2), ignore_border=True, st=(2, 2), padding=(0, 0), mode='max'}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0,2,3}(ConvOp{('imshp', (10, 12, 12)),('kshp', (8, 8)),('nkern', 40),('bsize', 20),('dx', 1),('dy', 1),('out_mode', 'valid'),('unroll_batch', 5),('unroll_kern', 2),('unroll_patch', False),('imshp_logical', (10, 12, 12)),('kshp_logical', (8, 8)),('kshp_logical_top_aligned', False)}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (ConvOp{('imshp', (10, 12, 12)),('kshp', (8, 8)),('nkern', 40),('bsize', 20),('dx', 1),('dy', 1),('out_mode', 'valid'),('unroll_batch', 5),('unroll_kern', 2),('unroll_patch', False),('imshp_logical', (10, 12, 12)),('kshp_logical', (8, 8)),('kshp_logical_top_aligned', False)}.0) of Op InplaceDimShuffle{1,0,2,3}(ConvOp{('imshp', (10, 12, 12)),('kshp', (8, 8)),('nkern', 40),('bsize', 20),('dx', 1),('dy', 1),('out_mode', 'valid'),('unroll_batch', 5),('unroll_kern', 2),('unroll_patch', False),('imshp_logical', (10, 12, 12)),('kshp_logical', (8, 8)),('kshp_logical_top_aligned', False)}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0,2,3}(MaxPoolGrad{ds=(2, 2), ignore_border=True, st=(2, 2), padding=(0, 0), mode='max'}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (MaxPoolGrad{ds=(2, 2), ignore_border=True, st=(2, 2), padding=(0, 0), mode='max'}.0) of Op InplaceDimShuffle{1,0,2,3}(MaxPoolGrad{ds=(2, 2), ignore_border=True, st=(2, 2), padding=(0, 0), mode='max'}.0) missing default value

ERROR (theano.gof.opt): Optimization failure due to: dimshuffle_as_view
ERROR (theano.gof.opt): node: DimShuffle{1,0,2,3}(ConvOp{('imshp', (10, 28, 28)),('kshp', (24, 24)),('nkern', 20),('bsize', 1),('dx', 1),('dy', 1),('out_mode', 'valid'),('unroll_batch', 1),('unroll_kern', 10),('unroll_patch', False),('imshp_logical', (10, 28, 28)),('kshp_logical', (24, 24)),('kshp_logical_top_aligned', False)}.0)
ERROR (theano.gof.opt): TRACEBACK:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/opt.py", line 1772, in process_node
    replacements = lopt.transform(node)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/tensor/opt.py", line 644, in dimshuffle_as_view
    v = new_op(*node.inputs)
  File "/home/ubuntu/miniconda2/lib/python2.7/site-packages/theano/gof/op.py", line 633, in __call__
    (i, ins, node))
ValueError: Cannot compute test value: input 0 (ConvOp{('imshp', (10, 28, 28)),('kshp', (24, 24)),('nkern', 20),('bsize', 1),('dx', 1),('dy', 1),('out_mode', 'valid'),('unroll_batch', 1),('unroll_kern', 10),('unroll_patch', False),('imshp_logical', (10, 28, 28)),('kshp_logical', (24, 24)),('kshp_logical_top_aligned', False)}.0) of Op InplaceDimShuffle{1,0,2,3}(ConvOp{('imshp', (10, 28, 28)),('kshp', (24, 24)),('nkern', 20),('bsize', 1),('dx', 1),('dy', 1),('out_mode', 'valid'),('unroll_batch', 1),('unroll_kern', 10),('unroll_patch', False),('imshp_logical', (10, 28, 28)),('kshp_logical', (24, 24)),('kshp_logical_top_aligned', False)}.0) missing default value











    






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Epoch 0: validation accuracy 98.63%
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The corresponding test accuracy is 98.81%
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Epoch 1: validation accuracy 99.01%
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The corresponding test accuracy is 99.07%
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Epoch 2: validation accuracy 99.17%
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Epoch 3: validation accuracy 99.25%
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Epoch 4: validation accuracy 99.29%
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The corresponding test accuracy is 99.44%
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Epoch 5: validation accuracy 99.34%
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Epoch 6: validation accuracy 99.39%
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The corresponding test accuracy is 99.54%
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Epoch 7: validation accuracy 99.37%
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Epoch 8: validation accuracy 99.40%
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Epoch 9: validation accuracy 99.46%
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Epoch 10: validation accuracy 99.49%
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Epoch 11: validation accuracy 99.40%
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Epoch 12: validation accuracy 99.41%
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Epoch 13: validation accuracy 99.35%
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Epoch 14: validation accuracy 99.47%
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Epoch 15: validation accuracy 99.45%
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Epoch 16: validation accuracy 99.54%
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The corresponding test accuracy is 99.61%
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Epoch 17: validation accuracy 99.47%
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Epoch 18: validation accuracy 99.45%
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Epoch 19: validation accuracy 99.47%
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Epoch 20: validation accuracy 99.54%
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Epoch 21: validation accuracy 99.50%
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Epoch 22: validation accuracy 99.47%
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Epoch 23: validation accuracy 99.52%
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Epoch 24: validation accuracy 99.46%
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Epoch 25: validation accuracy 99.57%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.56%
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Epoch 26: validation accuracy 99.56%
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Epoch 27: validation accuracy 99.53%
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Epoch 28: validation accuracy 99.56%
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Epoch 29: validation accuracy 99.60%
This is the best validation accuracy to date.
The corresponding test accuracy is 99.61%
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Epoch 30: validation accuracy 99.48%
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Epoch 31: validation accuracy 99.55%
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Epoch 32: validation accuracy 99.53%
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Epoch 33: validation accuracy 99.50%
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Epoch 34: validation accuracy 99.51%
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Epoch 35: validation accuracy 99.49%
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Epoch 36: validation accuracy 99.57%
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Epoch 37: validation accuracy 99.59%
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Epoch 38: validation accuracy 99.55%
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Epoch 39: validation accuracy 99.59%
Finished training network.
Best validation accuracy of 99.60% obtained at iteration 749999
Corresponding test accuracy of 99.61%

















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Content source: seanpquig/study-group

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