forked from akash13singh/lstm_anomaly_thesis
/
print_activations.py
64 lines (47 loc) · 2 KB
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print_activations.py
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from glob import glob
def get_activations(model, model_inputs, print_shape_only=False, layer_name=None):
import keras.backend as K
print('----- activations -----')
activations = []
inp = model.input
model_multi_inputs_cond = True
if not isinstance(inp, list):
# only one input! let's wrap it in a list.
inp = [inp]
model_multi_inputs_cond = False
outputs = [layer.output for layer in model.layers if
layer.name == layer_name or layer_name is None] # all layer outputs
funcs = [K.function(inp + [K.learning_phase()], [out]) for out in outputs] # evaluation functions
if model_multi_inputs_cond:
list_inputs = []
list_inputs.extend(model_inputs)
list_inputs.append(1.)
else:
list_inputs = [model_inputs, 1.]
# Learning phase. 1 = Test mode (no dropout or batch normalization)
# layer_outputs = [func([model_inputs, 1.])[0] for func in funcs]
layer_outputs = [func(list_inputs)[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
if __name__ == '__main__':
checkpoints = glob('checkpoints/*.h5')
# pip3 install natsort
from natsort import natsorted
from keras.models import load_model
if len(checkpoints) > 0:
checkpoints = natsorted(checkpoints)
assert len(checkpoints) != 0, 'No checkpoints found.'
checkpoint_file = checkpoints[-1]
print('Loading [{}]'.format(checkpoint_file))
model = load_model(checkpoint_file)
model.compile(optimizer='adam',
loss='mse ',
metrics=['accuracy'])
print(model.summary())
get_activations(model, x_test[0:1], print_shape_only=True) # with just one sample.
get_activations(model, x_test[0:200], print_shape_only=True) # with 200 samples.