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='categorical_crossentropy',
metrics=['accuracy'])
print(model.summary())
x_train, y_train, x_test, y_test = get_mnist_data()
# checking that the accuracy is the same as before 99% at the first epoch.
test_loss, test_acc = model.evaluate(x_test,
y_test,
verbose=1,
batch_size=128)
print('')
assert test_acc > 0.98
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.
import keras
from keras.layers import Dense
from keras.layers.recurrent import LSTM
from keras.models import Sequential
import utils
from data import get_mnist_data, num_classes
from keract import get_activations
if __name__ == '__main__':
x_train, _, _, _ = get_mnist_data()
# (60000, 28, 28, 1) to ((60000, 28, 28)
# LSTM has (batch, time_steps, input_dim)
x_train = x_train.squeeze()
model = Sequential()
model.add(LSTM(16, input_shape=(28, 28)))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
utils.print_names_and_shapes(get_activations(model, x_train[:128]))