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In [1]:



    


import tensorflow as tf
from keras.datasets import mnist
from keras.utils import np_utils
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['image.cmap'] = 'gray'
%matplotlib inline



    


















    






Using TensorFlow backend.

Loading the model.





In [2]:



    


# Restore model.
sess = tf.Session()    
#First let's load meta graph and restore weights
saver = tf.train.import_meta_graph('tf_mnist_model_layers/tf_mnist_model.ckpt.meta')
saver.restore(sess,tf.train.latest_checkpoint('tf_mnist_model_layers/'))



    


















    






INFO:tensorflow:Restoring parameters from tf_mnist_model_layers/tf_mnist_model.ckpt

Loading the dataset





In [3]:



    


dataset = mnist.load_data()
train_data = dataset[0][0] / 255
train_data = train_data[..., np.newaxis].astype('float32')
train_labels = np_utils.to_categorical(dataset[0][1]).astype('float32')
test_data = dataset[1][0] / 255
test_data = test_data[..., np.newaxis].astype('float32')
test_labels = np_utils.to_categorical(dataset[1][1]).astype('float32')
plt.imshow(train_data[0, ..., 0])



    


















    
Out[3]:








<matplotlib.image.AxesImage at 0x7fde82726518>










    
























In [4]:



    


# Get the first input tensor.
network_input = sess.graph.get_operations()[0].outputs[0]
network_input



    


















    
Out[4]:








<tf.Tensor 'Placeholder:0' shape=(?, 28, 28, 1) dtype=float32>

The TensorFlow graph

Look at all operations to find out the ones that are interesting for weights/activations.





In [5]:



    


sess.graph.get_operations()



    


















    
Out[5]:








[<tf.Operation 'Placeholder' type=Placeholder>,
 <tf.Operation 'Placeholder_1' type=Placeholder>,
 <tf.Operation 'training/input' type=Const>,
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 <tf.Operation 'conv2d_1/kernel/Initializer/truncated_normal/mean' type=Const>,
 <tf.Operation 'conv2d_1/kernel/Initializer/truncated_normal/stddev' type=Const>,
 <tf.Operation 'conv2d_1/kernel/Initializer/truncated_normal/TruncatedNormal' type=TruncatedNormal>,
 <tf.Operation 'conv2d_1/kernel/Initializer/truncated_normal/mul' type=Mul>,
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 <tf.Operation 'conv2d_1/kernel' type=VariableV2>,
 <tf.Operation 'conv2d_1/kernel/Assign' type=Assign>,
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 <tf.Operation 'conv2d_1/convolution/Shape' type=Const>,
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 <tf.Operation 'conv2d_1/convolution' type=Conv2D>,
 <tf.Operation 'conv2d_1/BiasAdd' type=BiasAdd>,
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 <tf.Operation 'Flatten/Const' type=Const>,
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 <tf.Operation 'dense_1/kernel/Assign' type=Assign>,
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 <tf.Operation 'dense_1/bias/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_1/bias' type=VariableV2>,
 <tf.Operation 'dense_1/bias/Assign' type=Assign>,
 <tf.Operation 'dense_1/bias/read' type=Identity>,
 <tf.Operation 'dense_1/MatMul' type=MatMul>,
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 <tf.Operation 'dropout_2/cond/dropout/Shape/Switch' type=Switch>,
 <tf.Operation 'dropout_2/cond/dropout/Shape' type=Shape>,
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 <tf.Operation 'dropout_2/cond/dropout/div' type=RealDiv>,
 <tf.Operation 'dropout_2/cond/dropout/mul' type=Mul>,
 <tf.Operation 'dropout_2/cond/Identity/Switch' type=Switch>,
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 <tf.Operation 'loss/Sub' type=Sub>,
 <tf.Operation 'loss/Slice/begin' type=Pack>,
 <tf.Operation 'loss/Slice/size' type=Const>,
 <tf.Operation 'loss/Slice' type=Slice>,
 <tf.Operation 'loss/concat/values_0' type=Const>,
 <tf.Operation 'loss/concat/axis' type=Const>,
 <tf.Operation 'loss/concat' type=ConcatV2>,
 <tf.Operation 'loss/Reshape' type=Reshape>,
 <tf.Operation 'loss/Rank_2' type=Const>,
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 <tf.Operation 'loss/Slice_1/size' type=Const>,
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 <tf.Operation 'gradients/dropout_1/cond/dropout/div_grad/RealDiv_2' type=RealDiv>,
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 <tf.Operation 'gradients/dropout_1/cond/dropout/div_grad/tuple/group_deps' type=NoOp>,
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 <tf.Operation 'gradients/conv2d_2/convolution_grad/Conv2DBackpropInput' type=Conv2DBackpropInput>,
 <tf.Operation 'gradients/conv2d_2/convolution_grad/Shape_1' type=Const>,
 <tf.Operation 'gradients/conv2d_2/convolution_grad/Conv2DBackpropFilter' type=Conv2DBackpropFilter>,
 <tf.Operation 'gradients/conv2d_2/convolution_grad/tuple/group_deps' type=NoOp>,
 <tf.Operation 'gradients/conv2d_2/convolution_grad/tuple/control_dependency' type=Identity>,
 <tf.Operation 'gradients/conv2d_2/convolution_grad/tuple/control_dependency_1' type=Identity>,
 <tf.Operation 'gradients/max_pooling2d_1/MaxPool_grad/MaxPoolGrad' type=MaxPoolGrad>,
 <tf.Operation 'gradients/conv2d_1/Relu_grad/ReluGrad' type=ReluGrad>,
 <tf.Operation 'gradients/conv2d_1/BiasAdd_grad/BiasAddGrad' type=BiasAddGrad>,
 <tf.Operation 'gradients/conv2d_1/BiasAdd_grad/tuple/group_deps' type=NoOp>,
 <tf.Operation 'gradients/conv2d_1/BiasAdd_grad/tuple/control_dependency' type=Identity>,
 <tf.Operation 'gradients/conv2d_1/BiasAdd_grad/tuple/control_dependency_1' type=Identity>,
 <tf.Operation 'gradients/conv2d_1/convolution_grad/Shape' type=Shape>,
 <tf.Operation 'gradients/conv2d_1/convolution_grad/Conv2DBackpropInput' type=Conv2DBackpropInput>,
 <tf.Operation 'gradients/conv2d_1/convolution_grad/Shape_1' type=Const>,
 <tf.Operation 'gradients/conv2d_1/convolution_grad/Conv2DBackpropFilter' type=Conv2DBackpropFilter>,
 <tf.Operation 'gradients/conv2d_1/convolution_grad/tuple/group_deps' type=NoOp>,
 <tf.Operation 'gradients/conv2d_1/convolution_grad/tuple/control_dependency' type=Identity>,
 <tf.Operation 'gradients/conv2d_1/convolution_grad/tuple/control_dependency_1' type=Identity>,
 <tf.Operation 'beta1_power/initial_value' type=Const>,
 <tf.Operation 'beta1_power' type=VariableV2>,
 <tf.Operation 'beta1_power/Assign' type=Assign>,
 <tf.Operation 'beta1_power/read' type=Identity>,
 <tf.Operation 'beta2_power/initial_value' type=Const>,
 <tf.Operation 'beta2_power' type=VariableV2>,
 <tf.Operation 'beta2_power/Assign' type=Assign>,
 <tf.Operation 'beta2_power/read' type=Identity>,
 <tf.Operation 'conv2d_1/kernel/Adam/Initializer/zeros' type=Const>,
 <tf.Operation 'conv2d_1/kernel/Adam' type=VariableV2>,
 <tf.Operation 'conv2d_1/kernel/Adam/Assign' type=Assign>,
 <tf.Operation 'conv2d_1/kernel/Adam/read' type=Identity>,
 <tf.Operation 'conv2d_1/kernel/Adam_1/Initializer/zeros' type=Const>,
 <tf.Operation 'conv2d_1/kernel/Adam_1' type=VariableV2>,
 <tf.Operation 'conv2d_1/kernel/Adam_1/Assign' type=Assign>,
 <tf.Operation 'conv2d_1/kernel/Adam_1/read' type=Identity>,
 <tf.Operation 'conv2d_1/bias/Adam/Initializer/zeros' type=Const>,
 <tf.Operation 'conv2d_1/bias/Adam' type=VariableV2>,
 <tf.Operation 'conv2d_1/bias/Adam/Assign' type=Assign>,
 <tf.Operation 'conv2d_1/bias/Adam/read' type=Identity>,
 <tf.Operation 'conv2d_1/bias/Adam_1/Initializer/zeros' type=Const>,
 <tf.Operation 'conv2d_1/bias/Adam_1' type=VariableV2>,
 <tf.Operation 'conv2d_1/bias/Adam_1/Assign' type=Assign>,
 <tf.Operation 'conv2d_1/bias/Adam_1/read' type=Identity>,
 <tf.Operation 'conv2d_2/kernel/Adam/Initializer/zeros' type=Const>,
 <tf.Operation 'conv2d_2/kernel/Adam' type=VariableV2>,
 <tf.Operation 'conv2d_2/kernel/Adam/Assign' type=Assign>,
 <tf.Operation 'conv2d_2/kernel/Adam/read' type=Identity>,
 <tf.Operation 'conv2d_2/kernel/Adam_1/Initializer/zeros' type=Const>,
 <tf.Operation 'conv2d_2/kernel/Adam_1' type=VariableV2>,
 <tf.Operation 'conv2d_2/kernel/Adam_1/Assign' type=Assign>,
 <tf.Operation 'conv2d_2/kernel/Adam_1/read' type=Identity>,
 <tf.Operation 'conv2d_2/bias/Adam/Initializer/zeros' type=Const>,
 <tf.Operation 'conv2d_2/bias/Adam' type=VariableV2>,
 <tf.Operation 'conv2d_2/bias/Adam/Assign' type=Assign>,
 <tf.Operation 'conv2d_2/bias/Adam/read' type=Identity>,
 <tf.Operation 'conv2d_2/bias/Adam_1/Initializer/zeros' type=Const>,
 <tf.Operation 'conv2d_2/bias/Adam_1' type=VariableV2>,
 <tf.Operation 'conv2d_2/bias/Adam_1/Assign' type=Assign>,
 <tf.Operation 'conv2d_2/bias/Adam_1/read' type=Identity>,
 <tf.Operation 'dense_1/kernel/Adam/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_1/kernel/Adam' type=VariableV2>,
 <tf.Operation 'dense_1/kernel/Adam/Assign' type=Assign>,
 <tf.Operation 'dense_1/kernel/Adam/read' type=Identity>,
 <tf.Operation 'dense_1/kernel/Adam_1/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_1/kernel/Adam_1' type=VariableV2>,
 <tf.Operation 'dense_1/kernel/Adam_1/Assign' type=Assign>,
 <tf.Operation 'dense_1/kernel/Adam_1/read' type=Identity>,
 <tf.Operation 'dense_1/bias/Adam/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_1/bias/Adam' type=VariableV2>,
 <tf.Operation 'dense_1/bias/Adam/Assign' type=Assign>,
 <tf.Operation 'dense_1/bias/Adam/read' type=Identity>,
 <tf.Operation 'dense_1/bias/Adam_1/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_1/bias/Adam_1' type=VariableV2>,
 <tf.Operation 'dense_1/bias/Adam_1/Assign' type=Assign>,
 <tf.Operation 'dense_1/bias/Adam_1/read' type=Identity>,
 <tf.Operation 'dense_2/kernel/Adam/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_2/kernel/Adam' type=VariableV2>,
 <tf.Operation 'dense_2/kernel/Adam/Assign' type=Assign>,
 <tf.Operation 'dense_2/kernel/Adam/read' type=Identity>,
 <tf.Operation 'dense_2/kernel/Adam_1/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_2/kernel/Adam_1' type=VariableV2>,
 <tf.Operation 'dense_2/kernel/Adam_1/Assign' type=Assign>,
 <tf.Operation 'dense_2/kernel/Adam_1/read' type=Identity>,
 <tf.Operation 'dense_2/bias/Adam/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_2/bias/Adam' type=VariableV2>,
 <tf.Operation 'dense_2/bias/Adam/Assign' type=Assign>,
 <tf.Operation 'dense_2/bias/Adam/read' type=Identity>,
 <tf.Operation 'dense_2/bias/Adam_1/Initializer/zeros' type=Const>,
 <tf.Operation 'dense_2/bias/Adam_1' type=VariableV2>,
 <tf.Operation 'dense_2/bias/Adam_1/Assign' type=Assign>,
 <tf.Operation 'dense_2/bias/Adam_1/read' type=Identity>,
 <tf.Operation 'Adam/learning_rate' type=Const>,
 <tf.Operation 'Adam/beta1' type=Const>,
 <tf.Operation 'Adam/beta2' type=Const>,
 <tf.Operation 'Adam/epsilon' type=Const>,
 <tf.Operation 'Adam/update_conv2d_1/kernel/ApplyAdam' type=ApplyAdam>,
 <tf.Operation 'Adam/update_conv2d_1/bias/ApplyAdam' type=ApplyAdam>,
 <tf.Operation 'Adam/update_conv2d_2/kernel/ApplyAdam' type=ApplyAdam>,
 <tf.Operation 'Adam/update_conv2d_2/bias/ApplyAdam' type=ApplyAdam>,
 <tf.Operation 'Adam/update_dense_1/kernel/ApplyAdam' type=ApplyAdam>,
 <tf.Operation 'Adam/update_dense_1/bias/ApplyAdam' type=ApplyAdam>,
 <tf.Operation 'Adam/update_dense_2/kernel/ApplyAdam' type=ApplyAdam>,
 <tf.Operation 'Adam/update_dense_2/bias/ApplyAdam' type=ApplyAdam>,
 <tf.Operation 'Adam/mul' type=Mul>,
 <tf.Operation 'Adam/Assign' type=Assign>,
 <tf.Operation 'Adam/mul_1' type=Mul>,
 <tf.Operation 'Adam/Assign_1' type=Assign>,
 <tf.Operation 'Adam' type=NoOp>,
 <tf.Operation 'ArgMax/dimension' type=Const>,
 <tf.Operation 'ArgMax' type=ArgMax>,
 <tf.Operation 'ArgMax_1/dimension' type=Const>,
 <tf.Operation 'ArgMax_1' type=ArgMax>,
 <tf.Operation 'Equal' type=Equal>,
 <tf.Operation 'Cast' type=Cast>,
 <tf.Operation 'Const' type=Const>,
 <tf.Operation 'Mean' type=Mean>,
 <tf.Operation 'init' type=NoOp>,
 <tf.Operation 'save/Const' type=Const>,
 <tf.Operation 'save/SaveV2/tensor_names' type=Const>,
 <tf.Operation 'save/SaveV2/shape_and_slices' type=Const>,
 <tf.Operation 'save/SaveV2' type=SaveV2>,
 <tf.Operation 'save/control_dependency' type=Identity>,
 <tf.Operation 'save/RestoreV2/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2' type=RestoreV2>,
 <tf.Operation 'save/Assign' type=Assign>,
 <tf.Operation 'save/RestoreV2_1/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_1/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_1' type=RestoreV2>,
 <tf.Operation 'save/Assign_1' type=Assign>,
 <tf.Operation 'save/RestoreV2_2/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_2/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_2' type=RestoreV2>,
 <tf.Operation 'save/Assign_2' type=Assign>,
 <tf.Operation 'save/RestoreV2_3/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_3/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_3' type=RestoreV2>,
 <tf.Operation 'save/Assign_3' type=Assign>,
 <tf.Operation 'save/RestoreV2_4/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_4/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_4' type=RestoreV2>,
 <tf.Operation 'save/Assign_4' type=Assign>,
 <tf.Operation 'save/RestoreV2_5/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_5/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_5' type=RestoreV2>,
 <tf.Operation 'save/Assign_5' type=Assign>,
 <tf.Operation 'save/RestoreV2_6/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_6/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_6' type=RestoreV2>,
 <tf.Operation 'save/Assign_6' type=Assign>,
 <tf.Operation 'save/RestoreV2_7/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_7/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_7' type=RestoreV2>,
 <tf.Operation 'save/Assign_7' type=Assign>,
 <tf.Operation 'save/RestoreV2_8/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_8/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_8' type=RestoreV2>,
 <tf.Operation 'save/Assign_8' type=Assign>,
 <tf.Operation 'save/RestoreV2_9/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_9/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_9' type=RestoreV2>,
 <tf.Operation 'save/Assign_9' type=Assign>,
 <tf.Operation 'save/RestoreV2_10/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_10/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_10' type=RestoreV2>,
 <tf.Operation 'save/Assign_10' type=Assign>,
 <tf.Operation 'save/RestoreV2_11/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_11/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_11' type=RestoreV2>,
 <tf.Operation 'save/Assign_11' type=Assign>,
 <tf.Operation 'save/RestoreV2_12/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_12/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_12' type=RestoreV2>,
 <tf.Operation 'save/Assign_12' type=Assign>,
 <tf.Operation 'save/RestoreV2_13/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_13/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_13' type=RestoreV2>,
 <tf.Operation 'save/Assign_13' type=Assign>,
 <tf.Operation 'save/RestoreV2_14/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_14/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_14' type=RestoreV2>,
 <tf.Operation 'save/Assign_14' type=Assign>,
 <tf.Operation 'save/RestoreV2_15/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_15/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_15' type=RestoreV2>,
 <tf.Operation 'save/Assign_15' type=Assign>,
 <tf.Operation 'save/RestoreV2_16/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_16/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_16' type=RestoreV2>,
 <tf.Operation 'save/Assign_16' type=Assign>,
 <tf.Operation 'save/RestoreV2_17/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_17/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_17' type=RestoreV2>,
 <tf.Operation 'save/Assign_17' type=Assign>,
 <tf.Operation 'save/RestoreV2_18/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_18/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_18' type=RestoreV2>,
 <tf.Operation 'save/Assign_18' type=Assign>,
 <tf.Operation 'save/RestoreV2_19/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_19/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_19' type=RestoreV2>,
 <tf.Operation 'save/Assign_19' type=Assign>,
 <tf.Operation 'save/RestoreV2_20/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_20/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_20' type=RestoreV2>,
 <tf.Operation 'save/Assign_20' type=Assign>,
 <tf.Operation 'save/RestoreV2_21/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_21/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_21' type=RestoreV2>,
 <tf.Operation 'save/Assign_21' type=Assign>,
 <tf.Operation 'save/RestoreV2_22/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_22/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_22' type=RestoreV2>,
 <tf.Operation 'save/Assign_22' type=Assign>,
 <tf.Operation 'save/RestoreV2_23/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_23/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_23' type=RestoreV2>,
 <tf.Operation 'save/Assign_23' type=Assign>,
 <tf.Operation 'save/RestoreV2_24/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_24/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_24' type=RestoreV2>,
 <tf.Operation 'save/Assign_24' type=Assign>,
 <tf.Operation 'save/RestoreV2_25/tensor_names' type=Const>,
 <tf.Operation 'save/RestoreV2_25/shape_and_slices' type=Const>,
 <tf.Operation 'save/RestoreV2_25' type=RestoreV2>,
 <tf.Operation 'save/Assign_25' type=Assign>,
 <tf.Operation 'save/restore_all' type=NoOp>]

TensorFlow exposes a graph API in which nodes correspond to operations and directed edges correspond to to tensors flowing from one operation to the next. Operations know their respective input and output tensors. Tensors know by which operations they get produced and for which operations they serve as input. c.f. https://www.tensorflow.org/extend/tool_developers/





In [6]:



    


conv_op = sess.graph.get_operation_by_name('conv2d_1/convolution')
print('inputs: ', list(conv_op.inputs))
print('outputs: ', list(conv_op.outputs))



    


















    






inputs:  [<tf.Tensor 'Placeholder:0' shape=(?, 28, 28, 1) dtype=float32>, <tf.Tensor 'conv2d_1/kernel/read:0' shape=(3, 3, 1, 32) dtype=float32>]
outputs:  [<tf.Tensor 'conv2d_1/convolution:0' shape=(?, 26, 26, 32) dtype=float32>]

















In [7]:



    


weight_tensor = conv_op.inputs[1]
weight_tensor



    


















    
Out[7]:








<tf.Tensor 'conv2d_1/kernel/read:0' shape=(3, 3, 1, 32) dtype=float32>

















In [10]:



    


# Producer ops for the tensor.
weight_tensor.op



    


















    
Out[10]:








<tf.Operation 'conv2d_1/kernel/read' type=Identity>

















In [11]:



    


# Consumer ops for the tensor.
weight_tensor.consumers()



    


















    
Out[11]:








[<tf.Operation 'conv2d_1/convolution' type=Conv2D>,
 <tf.Operation 'gradients/conv2d_1/convolution_grad/Conv2DBackpropInput' type=Conv2DBackpropInput>]

Getting the weights

For getting the weights we need to run the output tensor of operations that read the weights or the seconde input tensor of the actual MatMul/Conv operations.





In [18]:



    


weight_read_op = sess.graph.get_operation_by_name('conv2d_1/kernel/read')
weight_read_op



    


















    
Out[18]:








<tf.Operation 'conv2d_1/kernel/read' type=Identity>

















In [19]:



    


conv_op = sess.graph.get_operation_by_name('conv2d_1/convolution')
conv_op



    


















    
Out[19]:








<tf.Operation 'conv2d_1/convolution' type=Conv2D>

















In [20]:



    


weights_1 = sess.run(weight_read_op.outputs[0])
weights_2 = sess.run(conv_op.inputs[-1])
np.all(weights_1 == weights_2)



    


















    
Out[20]:








True

Looking at the actual kernels.





In [21]:



    


print(weights_1.shape)
for i in range(32):
    plt.imshow(weights_1[..., 0, i])
    plt.show()



    


















    






(3, 3, 1, 32)










    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    
























In [22]:



    


weights_1[..., 0, 0]



    


















    
Out[22]:








array([[-0.08219472,  0.01501322,  0.03917561],
       [ 0.13132864,  0.04290215, -0.04941976],
       [-0.05186096, -0.03700988,  0.18301845]], dtype=float32)

The same goes for the bias.





In [23]:



    


bias_add_op = sess.graph.get_operation_by_name('conv2d_1/BiasAdd')
bias_add_op



    


















    
Out[23]:








<tf.Operation 'conv2d_1/BiasAdd' type=BiasAdd>

















In [24]:



    


bias = sess.run(bias_add_op.inputs[-1])
bias



    


















    
Out[24]:








array([  2.55220849e-03,  -6.17763377e-04,   4.65408294e-03,
         1.22981519e-02,  -2.34646210e-03,  -2.23787036e-04,
        -3.72230512e-04,   2.21268404e-02,   2.86802351e-02,
        -5.76844672e-03,  -2.51290301e-04,   1.05109112e-02,
         4.26664911e-02,  -4.79708280e-04,  -1.47018407e-04,
         3.36357169e-02,   7.37800947e-05,   8.38877633e-04,
         7.63600506e-03,   2.40804045e-04,   8.00278410e-03,
        -1.05535528e-02,   3.01947235e-03,   9.05721821e-03,
         2.59847904e-04,   8.11665133e-03,  -1.28647589e-04,
         3.09362952e-02,  -2.05803127e-03,  -3.35510704e-04,
         1.95620786e-02,  -4.01624449e-04], dtype=float32)

Getting the activations and net inputs

Activations and net inputs can be found as the output tensors of the respective operations. Slices of the example code here are used to unit test the network package.





In [30]:



    


activation_op = sess.graph.get_operation_by_name('conv2d_1/Relu')
activation_op.outputs[0]



    


















    
Out[30]:








<tf.Tensor 'conv2d_1/Relu:0' shape=(?, 26, 26, 32) dtype=float32>

















In [31]:



    


activations = sess.run(activation_op.outputs[0], feed_dict={network_input: test_data[0:1]})
activations.shape



    


















    
Out[31]:








(1, 26, 26, 32)

Look at the activations





In [35]:



    


for i in range(32):
    plt.imshow(activations[0, ..., i])
    plt.show()



    


















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    
























In [36]:



    


activations[0, 5:10, 5:10, 0]



    


















    
Out[36]:








array([[ 0.12313882,  0.07273569,  0.05022647,  0.        ,  0.        ],
       [ 0.1309115 ,  0.14641027,  0.18909475,  0.19199452,  0.16788514],
       [ 0.09613925,  0.1401405 ,  0.14798687,  0.1795305 ,  0.20586434],
       [ 0.04382331,  0.0247027 ,  0.02338777,  0.        ,  0.02700226],
       [ 0.02401066,  0.        ,  0.        ,  0.01109774,  0.00698234]], dtype=float32)

















In [39]:



    


net_input_op = sess.graph.get_operation_by_name('conv2d_1/BiasAdd')
net_input_op.outputs[0]



    


















    
Out[39]:








<tf.Tensor 'conv2d_1/BiasAdd:0' shape=(?, 26, 26, 32) dtype=float32>

















In [40]:



    


net_inputs = sess.run(net_input_op.outputs[0], feed_dict={network_input: test_data[0:1]})
net_inputs.shape



    


















    
Out[40]:








(1, 26, 26, 32)

















In [41]:



    


for i in range(32):
    plt.imshow(net_inputs[0, ..., i])
    plt.show()



    


















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    

















    
























In [42]:



    


net_inputs[0, 5:10, 5:10, 0]



    


















    
Out[42]:








array([[ 0.12313882,  0.07273569,  0.05022647, -0.00863712, -0.01102792],
       [ 0.1309115 ,  0.14641027,  0.18909475,  0.19199452,  0.16788514],
       [ 0.09613925,  0.1401405 ,  0.14798687,  0.1795305 ,  0.20586434],
       [ 0.04382331,  0.0247027 ,  0.02338777, -0.00067293,  0.02700226],
       [ 0.02401066, -0.00127091, -0.01244084,  0.01109774,  0.00698234]], dtype=float32)

Getting input and output shape

For each tensor the shape can be retrieved as a TensorShape object or as a list.





In [64]:



    


conv_op.inputs[0].shape



    


















    
Out[64]:








TensorShape([Dimension(None), Dimension(28), Dimension(28), Dimension(1)])

















In [65]:



    


conv_op.inputs[0].shape.as_list()



    


















    
Out[65]:








[None, 28, 28, 1]

Getting layer properties

Internally TensorFlow represents graph as protobufs that only define nodes with their inputs. This definition can either be retrieved as globally graph.graph_def as as op.node_def for every single operation.





In [50]:



    


sess.graph_def



    


















    
Out[50]:








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In [51]:



    


activation_op.node_def



    


















    
Out[51]:








name: "conv2d_1/Relu"
op: "Relu"
input: "conv2d_1/BiasAdd"
attr {
  key: "T"
  value {
    type: DT_FLOAT
  }
}

Kernel size

Kernel size is not defined in the protobuf, but has to be read from the weight tensor shape.





In [53]:



    


read_op = sess.graph.get_operation_by_name('conv2d_2/kernel/read')
read_op.outputs[0].shape.as_list()[:2]



    


















    
Out[53]:








[3, 3]

Padding





In [55]:



    


conv_op.node_def



    


















    
Out[55]:








name: "conv2d_1/convolution"
op: "Conv2D"
input: "Placeholder"
input: "conv2d_1/kernel/read"
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attr {
  key: "use_cudnn_on_gpu"
  value {
    b: true
  }
}

















In [56]:



    


# The padding value has to be decoded from bytes into a string.
conv_op.node_def.attr['padding'].s.decode('utf8')



    


















    
Out[56]:








'VALID'

Strides





In [57]:



    


strides = conv_op.node_def.attr['strides']
strides.list.i[1], strides.list.i[2]



    


















    
Out[57]:








(1, 1)

Pool size

Pool size is defined as ksize in the maxpool definition.





In [58]:



    


max_pool_op = sess.graph.get_operation_by_name('max_pooling2d_1/MaxPool')
max_pool_op.node_def



    


















    
Out[58]:








name: "max_pooling2d_1/MaxPool"
op: "MaxPool"
input: "conv2d_1/Relu"
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attr {
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      i: 2
      i: 2
      i: 1
    }
  }
}

















In [63]:



    


kernel = max_pool_op.node_def.attr['ksize']
kernel.list.i[1], kernel.list.i[2]



    


















    
Out[63]:








(2, 2)

















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Content source: Petr-By/qtpyvis

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