from __future__ import print_function import numpy as np import pytest import string from keras.utils.test_utils import get_test_data from keras.utils.np_utils import to_categorical from keras.models import Sequential from keras import layers, optimizers import keras.backend as K import keras def test_temporal_classification(): ''' Classify temporal sequences of float numbers of length 3 into 2 classes using single layer of GRU units and softmax applied to the last activations of the units ''' np.random.seed(1337) (x_train, y_train), (x_test, y_test) = get_test_data(num_train=200, num_test=20, input_shape=(3, 4), classification=True, num_classes=2) y_train = to_categorical(y_train) y_test = to_categorical(y_test) model = Sequential() model.add(layers.GRU(8, input_shape=(x_train.shape[1], x_train.shape[2]))) model.add(layers.Dense(y_train.shape[-1], activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) model.summary() history = model.fit(x_train, y_train, epochs=5, batch_size=10, validation_data=(x_test, y_test), verbose=0) assert(history.history['accuracy'][-1] >= 0.8) config = model.get_config() model = Sequential.from_config(config) def test_temporal_classification_functional(): ''' Classify temporal sequences of float numbers of length 3 into 2 classes using single layer of GRU units and softmax applied to the last activations of the units ''' np.random.seed(1337) (x_train, y_train), (x_test, y_test) = get_test_data(num_train=200, num_test=20, input_shape=(3, 4), classification=True, num_classes=2) y_train = to_categorical(y_train) y_test = to_categorical(y_test) inputs = layers.Input(shape=(x_train.shape[1], x_train.shape[2])) x = layers.SimpleRNN(8)(inputs) outputs = layers.Dense(y_train.shape[-1], activation='softmax')(x) model = keras.models.Model(inputs, outputs) model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) history = model.fit(x_train, y_train, epochs=5, batch_size=10, validation_data=(x_test, y_test), verbose=0) assert(history.history['accuracy'][-1] >= 0.75) def test_temporal_regression(): ''' Predict float numbers (regression) based on sequences of float numbers of length 3 using a single layer of GRU units ''' np.random.seed(1337) (x_train, y_train), (x_test, y_test) = get_test_data(num_train=200, num_test=20, input_shape=(3, 5), output_shape=(2,), classification=False) model = Sequential() model.add(layers.LSTM(y_train.shape[-1], input_shape=(x_train.shape[1], x_train.shape[2]))) model.compile(loss='hinge', optimizer='adam') history = model.fit(x_train, y_train, epochs=5, batch_size=16, validation_data=(x_test, y_test), verbose=0) assert(history.history['loss'][-1] < 1.) def test_3d_to_3d(): ''' Apply a same Dense layer for each element of time dimension of the input and make predictions of the output sequence elements. This does not make use of the temporal structure of the sequence (see TimeDistributedDense for more details) ''' np.random.seed(1337) (x_train, y_train), (x_test, y_test) = get_test_data(num_train=100, num_test=20, input_shape=(3, 5), output_shape=(3, 5), classification=False) model = Sequential() model.add(layers.TimeDistributed( layers.Dense(y_train.shape[-1]), input_shape=x_train.shape[1:3])) model.compile(loss='hinge', optimizer='rmsprop') history = model.fit(x_train, y_train, epochs=20, batch_size=16, validation_data=(x_test, y_test), verbose=0) assert(history.history['loss'][-1] < 1.) def test_stacked_lstm_char_prediction(): ''' Learn alphabetical char sequence with stacked LSTM. Predict the whole alphabet based on the first two letters ('ab' -> 'ab...z') See non-toy example in examples/lstm_text_generation.py ''' # generate alphabet: # http://stackoverflow.com/questions/16060899/alphabet-range-python alphabet = string.ascii_lowercase number_of_chars = len(alphabet) # generate char sequences of length 'sequence_length' out of alphabet and # store the next char as label (e.g. 'ab'->'c') sequence_length = 2 sentences = [alphabet[i: i + sequence_length] for i in range(len(alphabet) - sequence_length)] next_chars = [alphabet[i + sequence_length] for i in range(len(alphabet) - sequence_length)] # Transform sequences and labels into 'one-hot' encoding x = np.zeros((len(sentences), sequence_length, number_of_chars), dtype=np.bool) y = np.zeros((len(sentences), number_of_chars), dtype=np.bool) for i, sentence in enumerate(sentences): for t, char in enumerate(sentence): x[i, t, ord(char) - ord('a')] = 1 y[i, ord(next_chars[i]) - ord('a')] = 1 # learn the alphabet with stacked LSTM model = Sequential([ layers.LSTM(16, return_sequences=True, input_shape=(sequence_length, number_of_chars)), layers.LSTM(16, return_sequences=False), layers.Dense(number_of_chars, activation='softmax') ]) model.compile(loss='categorical_crossentropy', optimizer='adam') model.fit(x, y, batch_size=1, epochs=60, verbose=1) # prime the model with 'ab' sequence and let it generate the learned alphabet sentence = alphabet[:sequence_length] generated = sentence for iteration in range(number_of_chars - sequence_length): x = np.zeros((1, sequence_length, number_of_chars)) for t, char in enumerate(sentence): x[0, t, ord(char) - ord('a')] = 1. preds = model.predict(x, verbose=0)[0] next_char = chr(np.argmax(preds) + ord('a')) generated += next_char sentence = sentence[1:] + next_char # check that it did generate the alphabet correctly assert(generated == alphabet) @pytest.mark.skipif(K.backend() != 'tensorflow', reason='Requires TF backend') def test_embedding_with_clipnorm(): model = Sequential() model.add(layers.Embedding(input_dim=1, output_dim=1)) model.compile(optimizer=optimizers.SGD(clipnorm=0.1), loss='mse') model.fit(np.array([[0]]), np.array([[[0.5]]]), epochs=1) if __name__ == '__main__': pytest.main([__file__])