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import numpy as np
def sum_squares(N):
    return <student.Implement_me()>


    

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%%time
sum_squares(10**8)


    

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import theano
import theano.tensor as T


    

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#I gonna be function parameter
N = T.scalar("a dimension",dtype='int32')


#i am a recipe on how to produce sum of squares of arange of N given N
result = (T.arange(N)**2).sum()

#Compiling the recipe of computing "result" given N
sum_function = theano.function(inputs = [N],outputs=result)


    

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%%time
sum_function(10**8)


    

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#Inputs
example_input_integer = T.scalar("scalar input",dtype='float32')

example_input_tensor = T.tensor4("four dimensional tensor input") #dtype = theano.config.floatX by default
#не бойся, тензор нам не пригодится



input_vector = T.vector("my vector", dtype='int32') # vector of integers


    

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#Transformations

#transofrmation: elementwise multiplication
double_the_vector = input_vector*2

#elementwise cosine
elementwise_cosine = T.cos(input_vector)

#difference between squared vector and vector itself
vector_squares = input_vector**2 - input_vector


    

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#Practice time:
#create two vectors of size float32
my_vector = student.init_float32_vector()
my_vector2 = student.init_one_more_such_vector()


    

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#Write a transformation(recipe):
#(vec1)*(vec2) / (sin(vec1) +1)
my_transformation = student.implementwhatwaswrittenabove()


    

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print( my_transformation)
#it's okay it aint a number


    

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#What's inside the transformation
theano.printing.debugprint(my_transformation)


    

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inputs = [<two vectors that my_transformation depends on>]
outputs = [<What do we compute (can be a list of several transformation)>]

# The next lines compile a function that takes two vectors and computes your transformation
my_function = theano.function(
    inputs,outputs,
    allow_input_downcast=True #automatic type casting for input parameters (e.g. float64 -> float32)
 )


    

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#using function with, lists:
print "using python lists:"
print my_function([1,2,3],[4,5,6])
print

#Or using numpy arrays:
#btw, that 'float' dtype is casted to secong parameter dtype which is float32
print "using numpy arrays:"
print my_function(np.arange(10),
                  np.linspace(5,6,10,dtype='float'))


    

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#a dictionary of inputs
my_function_inputs = {
    my_vector:[1,2,3],
    my_vector2:[4,5,6]
}

# evaluate my_transformation
# has to match with compiled function output
print my_transformation.eval(my_function_inputs)


# can compute transformations on the fly
print ("add 2 vectors", (my_vector + my_vector2).eval(my_function_inputs))

#!WARNING! if your transformation only depends on some inputs,
#do not provide the rest of them
print ("vector's shape:", my_vector.shape.eval({
        my_vector:[1,2,3]
    }))


    

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# Quest #1 - implement a function that computes a mean squared error of two input vectors
# Your function has to take 2 vectors and return a single number

<student.define_inputs_and_transformations()>

compute_mse =<student.compile_function()>


    

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# Tests
from sklearn.metrics import mean_squared_error

for n in [1,5,10,10**3]:
    
    elems = [np.arange(n),np.arange(n,0,-1), np.zeros(n),
             np.ones(n),np.random.random(n),np.random.randint(100,size=n)]
    
    for el in elems:
        for el_2 in elems:
            true_mse = np.array(mean_squared_error(el,el_2))
            my_mse = compute_mse(el,el_2)
            if not np.allclose(true_mse,my_mse):
                print ('Wrong result:')
                print ('mse(%s,%s)'%(el,el_2))
                print ("should be: %f, but your function returned %f"%(true_mse,my_mse))
                raise ValueError("Что-то не так")

print ("All tests passed")


    

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#creating shared variable
shared_vector_1 = theano.shared(np.ones(10,dtype='float64'))


    

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#evaluating shared variable (outside symbolicd graph)
print ("initial value",shared_vector_1.get_value())

# within symbolic graph you use them just as any other inout or transformation, not "get value" needed


    

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#setting new value
shared_vector_1.set_value( np.arange(5) )

#getting that new value
print ("new value", shared_vector_1.get_value())

#Note that the vector changed shape
#This is entirely allowed... unless your graph is hard-wired to work with some fixed shape


    

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# Write a recipe (transformation) that computes an elementwise transformation of shared_vector and input_scalar
#Compile as a function of input_scalar

input_scalar = T.scalar('coefficient',dtype='float32')

scalar_times_shared = <student.write_recipe()>


shared_times_n = <student.compile_function()>


    

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print "shared:", shared_vector_1.get_value()

print "shared_times_n(5)",shared_times_n(5)

print "shared_times_n(-0.5)",shared_times_n(-0.5)


    

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#Changing value of vector 1 (output should change)
shared_vector_1.set_value([-1,0,1])
print "shared:", shared_vector_1.get_value()

print "shared_times_n(5)",shared_times_n(5)

print "shared_times_n(-0.5)",shared_times_n(-0.5)


    

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my_scalar = T.scalar(name='input',dtype='float64')

scalar_squared = T.sum(my_scalar**2)

#a derivative of v_squared by my_vector
derivative = T.grad(scalar_squared,my_scalar)

fun = theano.function([my_scalar],scalar_squared)
grad = theano.function([my_scalar],derivative)


    

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import matplotlib.pyplot as plt
%matplotlib inline


x = np.linspace(-3,3)
x_squared = list(map(fun,x))
x_squared_der = list(map(grad,x))

plt.plot(x, x_squared,label="x^2")
plt.plot(x, x_squared_der, label="derivative")
plt.legend()


    

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my_vector = T.vector('float64')

#Compute the gradient of the next weird function over my_scalar and my_vector
#warning! Trying to understand the meaning of that function may result in permanent brain damage

weird_psychotic_function = ((my_vector+my_scalar)**(1+T.var(my_vector)) +1./T.arcsinh(my_scalar)).mean()/(my_scalar**2 +1) + 0.01*T.sin(2*my_scalar**1.5)*(T.sum(my_vector)* my_scalar**2)*T.exp((my_scalar-4)**2)/(1+T.exp((my_scalar-4)**2))*(1.-(T.exp(-(my_scalar-4)**2))/(1+T.exp(-(my_scalar-4)**2)))**2


der_by_scalar,der_by_vector = <student.compute_grad_over_scalar_and_vector()>


compute_weird_function = theano.function([my_scalar,my_vector],weird_psychotic_function)
compute_der_by_scalar = theano.function([my_scalar,my_vector],der_by_scalar)


    

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#Plotting your derivative
vector_0 = [1,2,3]

scalar_space = np.linspace(0,7)

y = [compute_weird_function(x,vector_0) for x in scalar_space]
plt.plot(scalar_space,y,label='function')
y_der_by_scalar = [compute_der_by_scalar(x,vector_0) for x in scalar_space]
plt.plot(scalar_space,y_der_by_scalar,label='derivative')
plt.grid();plt.legend()


    

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# Multiply shared vector by a number and save the product back into shared vector

inputs = [input_scalar]
outputs = [scalar_times_shared] #return vector times scalar

my_updates = {
    shared_vector_1:scalar_times_shared #and write this same result bach into shared_vector_1
}

compute_and_save = theano.function(inputs, outputs, updates=my_updates)


    

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shared_vector_1.set_value(np.arange(5))

#initial shared_vector_1
print ("initial shared value:" ,shared_vector_1.get_value())

# evaluating the function (shared_vector_1 will be changed)
print ("compute_and_save(2) returns",compute_and_save(2))

#evaluate new shared_vector_1
print ("new shared value:" ,shared_vector_1.get_value())


    

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from sklearn.datasets import load_digits
mnist = load_digits(2)

X,y = mnist.data, mnist.target


print ("y [shape - %s]:"%(str(y.shape)),y[:10])

print ("X [shape - %s]:"%(str(X.shape)))
print (X[:3])
print (y[:10])


    

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# inputs and shareds
shared_weights = <student.code_me()>
input_X = <student.code_me()>
input_y = <student.code_me()>


    

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predicted_y = <predicted probabilities for input_X>


loss = <logistic loss (scalar, mean over sample)>


grad = <gradient of loss over model weights>


updates = {
    shared_weights: <new weights after gradient step>
}


    

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train_function = <compile function that takes X and y, returns log loss and updates weights>

predict_function = <compile function that takes X and computes probabilities of y>


    

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from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y)


    

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from sklearn.metrics import roc_auc_score

for i in range(5):
    loss_i = train_function(X_train,y_train)
    print ("loss at iter %i:%.4f"%(i,loss_i))
    print ("train auc:",roc_auc_score(y_train,predict_function(X_train)))
    print ("test auc:",roc_auc_score(y_test,predict_function(X_test)))

    
print ("resulting weights:")
plt.imshow(shared_weights.get_value().reshape(8,-1))
plt.colorbar()


    

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from mnist import load_dataset
X_train,y_train,X_val,y_val,X_test,y_test = load_dataset()

print X_train.shape,y_train.shape


    

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import lasagne

input_X = T.tensor4("X")

#input dimention (None means "Arbitrary" and only works at  the first axes [samples])
input_shape = [None,1,28,28]

target_y = T.vector("target Y integer",dtype='int32')


    

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#Input layer (auxilary)
input_layer = lasagne.layers.InputLayer(shape = input_shape,input_var=input_X)

#fully connected layer, that takes input layer and applies 50 neurons to it.
# nonlinearity here is sigmoid as in logistic regression
# you can give a name to each layer (optional)
dense_1 = lasagne.layers.DenseLayer(input_layer,num_units=50,
                                   nonlinearity = lasagne.nonlinearities.sigmoid,
                                   name = "hidden_dense_layer")

#fully connected output layer that takes dense_1 as input and has 10 neurons (1 for each digit)
#We use softmax nonlinearity to make probabilities add up to 1
dense_output = lasagne.layers.DenseLayer(dense_1,num_units = 10,
                                        nonlinearity = lasagne.nonlinearities.softmax,
                                        name='output')


    

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#network prediction (theano-transformation)
y_predicted = lasagne.layers.get_output(dense_output)


    

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#all network weights (shared variables)
all_weights = lasagne.layers.get_all_params(dense_output)
print (all_weights)


    

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#Mean categorical crossentropy as a loss function - similar to logistic loss but for multiclass targets
loss = lasagne.objectives.categorical_crossentropy(y_predicted,target_y).mean()

#prediction accuracy
accuracy = lasagne.objectives.categorical_accuracy(y_predicted,target_y).mean()

#This function computes gradient AND composes weight updates just like you did earlier
updates_sgd = lasagne.updates.sgd(loss, all_weights,learning_rate=0.01)


    

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#function that computes loss and updates weights
train_fun = theano.function([input_X,target_y],[loss,accuracy],updates= updates_sgd)

#function that just computes accuracy
accuracy_fun = theano.function([input_X,target_y],accuracy)


    

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# An auxilary function that returns mini-batches for neural network training

#Parameters
# X - a tensor of images with shape (many, 1, 28, 28), e.g. X_train
# y - a vector of answers for corresponding images e.g. Y_train
#batch_size - a single number - the intended size of each batches

#What do need to implement
# 1) Shuffle data
# - Gotta shuffle X and y the same way not to break the correspondence between X_i and y_i
# 3) Split data into minibatches of batch_size
# - If data size is not a multiple of batch_size, make one last batch smaller.
# 4) return a list (or an iterator) of pairs
# - (подгруппа картинок, ответы из y на эту подгруппу)
def iterate_minibatches(X, y, batchsize):
    
    <return an iterable of (X_batch, y_batch)  batches of images and answers for them>
    
        
        
#
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# You feel lost and wish you stayed home tonight?
# Go search for a similar function at
# https://github.com/Lasagne/Lasagne/blob/master/examples/mnist.py


    

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import time

num_epochs = 100 #amount of passes through the data

batch_size = 50 #number of samples processed at each function call

for epoch in range(num_epochs):
    # In each epoch, we do a full pass over the training data:
    train_err = 0
    train_acc = 0
    train_batches = 0
    start_time = time.time()
    for batch in iterate_minibatches(X_train, y_train,batch_size):
        inputs, targets = batch
        train_err_batch, train_acc_batch= train_fun(inputs, targets)
        train_err += train_err_batch
        train_acc += train_acc_batch
        train_batches += 1

    # And a full pass over the validation data:
    val_acc = 0
    val_batches = 0
    for batch in iterate_minibatches(X_val, y_val, batch_size):
        inputs, targets = batch
        val_acc += accuracy_fun(inputs, targets)
        val_batches += 1

    
    # Then we print the results for this epoch:
    print("Epoch {} of {} took {:.3f}s".format(
        epoch + 1, num_epochs, time.time() - start_time))

    print("  training loss (in-iteration):\t\t{:.6f}".format(train_err / train_batches))
    print("  train accuracy:\t\t{:.2f} %".format(
        train_acc / train_batches * 100))
    print("  validation accuracy:\t\t{:.2f} %".format(
        val_acc / val_batches * 100))


    

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test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test, y_test, 500):
    inputs, targets = batch
    acc = accuracy_fun(inputs, targets)
    test_acc += acc
    test_batches += 1
print("Final results:")
print("  test accuracy:\t\t{:.2f} %".format(
    test_acc / test_batches * 100))

if test_acc / test_batches * 100 > 99:
    print ("Achievement unlocked: 80lvl Warlock!")
else:
    print ("We need more magic!")


    

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from mnist import load_dataset
X_train,y_train,X_val,y_val,X_test,y_test = load_dataset()

print X_train.shape,y_train.shape


    

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import lasagne

input_X = T.tensor4("X")

#input dimention (None means "Arbitrary" and only works at  the first axes [samples])
input_shape = [None,1,28,28]

target_y = T.vector("target Y integer",dtype='int32')


    

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#Input layer (auxilary)
input_layer = lasagne.layers.InputLayer(shape = input_shape,input_var=input_X)

<student.code_neural_network_architecture()>

dense_output = <your network output>


    

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# Network predictions (theano-transformation)
y_predicted = lasagne.layers.get_output(dense_output)


    

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#All weights (shared-varaibles)
# "trainable" flag means not to return auxilary params like batch mean (for batch normalization)
all_weights = lasagne.layers.get_all_params(dense_output,trainable=True)
print (all_weights)


    

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#loss function
loss = <loss function>

#<optionally add regularization>

accuracy = <mean accuracy score for evaluation> 

#weight updates
updates = <try different update methods>


    

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#A function that accepts X and y, returns loss functions and performs weight updates
train_fun = theano.function([input_X,target_y],[loss,accuracy],updates= updates_sgd)

#A function that just computes accuracy given X and y
accuracy_fun = theano.function([input_X,target_y],accuracy)


    

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#итерации обучения

num_epochs = <how many times to iterate over the entire training set>

batch_size = <how many samples are processed at a single function call>

for epoch in range(num_epochs):
    # In each epoch, we do a full pass over the training data:
    train_err = 0
    train_acc = 0
    train_batches = 0
    start_time = time.time()
    for batch in iterate_minibatches(X_train, y_train,batch_size):
        inputs, targets = batch
        train_err_batch, train_acc_batch= train_fun(inputs, targets)
        train_err += train_err_batch
        train_acc += train_acc_batch
        train_batches += 1

    # And a full pass over the validation data:
    val_acc = 0
    val_batches = 0
    for batch in iterate_minibatches(X_val, y_val, batch_size):
        inputs, targets = batch
        val_acc += accuracy_fun(inputs, targets)
        val_batches += 1

    
    # Then we print the results for this epoch:
    print("Epoch {} of {} took {:.3f}s".format(
        epoch + 1, num_epochs, time.time() - start_time))

    print("  training loss (in-iteration):\t\t{:.6f}".format(train_err / train_batches))
    print("  train accuracy:\t\t{:.2f} %".format(
        train_acc / train_batches * 100))
    print("  validation accuracy:\t\t{:.2f} %".format(
        val_acc / val_batches * 100))


    

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test_acc = 0
test_batches = 0
for batch in iterate_minibatches(X_test, y_test, 500):
    inputs, targets = batch
    acc = accuracy_fun(inputs, targets)
    test_acc += acc
    test_batches += 1
print("Final results:")
print("  test accuracy:\t\t{:.2f} %".format(
    test_acc / test_batches * 100))

if test_acc / test_batches * 100 > 99:
    print ("Achievement unlocked: 80lvl Warlock!")
else:
    print ("We need more magic!")


    

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