import keras.backend as K
import numpy as np
def get_activations(model, inputs, print_shape_only=False, layer_name=None):
# Documentation is available online on Github at the address below.
# From: https://github.com/philipperemy/keras-visualize-activations
print('----- activations -----')
activations = []
inp = model.input
if layer_name is None:
outputs = [layer.output for layer in model.layers]
else:
outputs = [layer.output for layer in model.layers if layer.name == layer_name] # all layer outputs
funcs = [K.function([inp] + [K.learning_phase()], [out]) for out in outputs] # evaluation functions
layer_outputs = [func([inputs, 1.])[0] for func in funcs]
for layer_activations in layer_outputs:
activations.append(layer_activations)
if print_shape_only:
print(layer_activations.shape)
else:
print(layer_activations)
return activations
def get_data(n, input_dim, attention_column=1):
"""
Data generation. x is purely random except that it's first value equals the target y.
In practice, the network should learn that the target = x[attention_column].
Therefore, most of its attention should be focused on the value addressed by attention_column.
:param n: the number of samples to retrieve.
:param input_dim: the number of dimensions of each element in the series.
:param attention_column: the column linked to the target. Everything else is purely random.
:return: x: model inputs, y: model targets
"""
x = np.random.standard_normal(size=(n, input_dim))
y = np.random.randint(low=0, high=2, size=(n, 1))
x[:, attention_column] = y[:, 0]
return x, y
def get_data_recurrent(n, time_steps, input_dim, attention_column=10):
"""
Data generation. x is purely random except that it's first value equals the target y.
In practice, the network should learn that the target = x[attention_column].
Therefore, most of its attention should be focused on the value addressed by attention_column.
:param n: the number of samples to retrieve.
:param time_steps: the number of time steps of your series.
:param input_dim: the number of dimensions of each element in the series.
:param attention_column: the column linked to the target. Everything else is purely random.
:return: x: model inputs, y: model targets
"""
x = np.random.standard_normal(size=(n, time_steps, input_dim))
y = np.random.randint(low=0, high=2, size=(n, 1))
x[:, attention_column, :] = np.tile(y[:], (1, input_dim))
return x, y
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