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]))