def test_load_persist(self):
# define the model.
model = Sequential()
model.add(Dense(16, input_shape=(10, )))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy')
# fetch activations.
x = np.ones((2, 10))
activations = get_activations(model, x)
# persist the activations to the disk.
output = 'activations.json'
persist_to_json_file(activations, output)
# read them from the disk.
activations2 = load_activations_from_json_file(output)
for a1, a2 in zip(list(activations.values()),
list(activations2.values())):
np.testing.assert_almost_equal(a1, a2)
import numpy as np from keras import Sequential from keras.layers import Dropout, Dense from keract import get_activations, persist_to_json_file, load_activations_from_json_file # define the model. model = Sequential() model.add(Dense(16, input_shape=(10, ))) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) model.compile(optimizer='adam', loss='categorical_crossentropy') # fetch activations. x = np.ones((2, 10)) activations = get_activations(model, x) # persist the activations to the disk. output = 'activations.json' persist_to_json_file(activations, output) # read them from the disk. activations2 = load_activations_from_json_file(output) # print them. print(list(activations.keys())) print(list(activations2.keys()))