def test_TensorBoard(): import shutil import tensorflow as tf import keras.backend.tensorflow_backend as KTF old_session = KTF.get_session() filepath = './logs' (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples, nb_test=test_samples, input_shape=(input_dim,), classification=True, nb_class=nb_class) y_test = np_utils.to_categorical(y_test) y_train = np_utils.to_categorical(y_train) def data_generator(train): if train: max_batch_index = len(X_train) // batch_size else: max_batch_index = len(X_test) // batch_size i = 0 while 1: if train: yield (X_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size]) else: yield (X_test[i * batch_size: (i + 1) * batch_size], y_test[i * batch_size: (i + 1) * batch_size]) i += 1 i = i % max_batch_index def data_generator_graph(train): while 1: if train: yield {'X_vars': X_train, 'output': y_train} else: yield {'X_vars': X_test, 'output': y_test} # case 1 Sequential with tf.Graph().as_default(): session = tf.Session('') KTF.set_session(session) model = Sequential() model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu')) model.add(Dense(nb_class, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1) cbks = [tsb] # fit with validation data model.fit(X_train, y_train, batch_size=batch_size, validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2) # fit with validation data and accuracy model.fit(X_train, y_train, batch_size=batch_size, validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2) # fit generator with validation data model.fit_generator(data_generator(True), len(X_train), nb_epoch=2, validation_data=(X_test, y_test), callbacks=cbks) # fit generator without validation data model.fit_generator(data_generator(True), len(X_train), nb_epoch=2, callbacks=cbks) # fit generator with validation data and accuracy model.fit_generator(data_generator(True), len(X_train), nb_epoch=2, validation_data=(X_test, y_test), callbacks=cbks) # fit generator without validation data and accuracy model.fit_generator(data_generator(True), len(X_train), nb_epoch=2, callbacks=cbks) assert os.path.exists(filepath) shutil.rmtree(filepath) # case 2 Graph with tf.Graph().as_default(): session = tf.Session('') KTF.set_session(session) model = Graph() model.add_input(name='X_vars', input_shape=(input_dim,)) model.add_node(Dense(nb_hidden, activation="sigmoid"), name='Dense1', input='X_vars') model.add_node(Dense(nb_class, activation="softmax"), name='last_dense', input='Dense1') model.add_output(name='output', input='last_dense') model.compile(optimizer='sgd', loss={'output': 'mse'}) tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1) cbks = [tsb] # fit with validation model.fit({'X_vars': X_train, 'output': y_train}, batch_size=batch_size, validation_data={'X_vars': X_test, 'output': y_test}, callbacks=cbks, nb_epoch=2) # fit wo validation model.fit({'X_vars': X_train, 'output': y_train}, batch_size=batch_size, callbacks=cbks, nb_epoch=2) # fit generator with validation model.fit_generator(data_generator_graph(True), 1000, nb_epoch=2, validation_data={'X_vars': X_test, 'output': y_test}, callbacks=cbks) # fit generator wo validation model.fit_generator(data_generator_graph(True), 1000, nb_epoch=2, callbacks=cbks) assert os.path.exists(filepath) shutil.rmtree(filepath) KTF.set_session(old_session)
def test_TensorBoard(): import shutil import tensorflow as tf import keras.backend.tensorflow_backend as KTF old_session = KTF._get_session() filepath = './logs' (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples, nb_test=test_samples, input_shape=(input_dim,), classification=True, nb_class=nb_class) y_test = np_utils.to_categorical(y_test) y_train = np_utils.to_categorical(y_train) # case 1 Sequential wo accuracy with tf.Graph().as_default(): session = tf.Session('') KTF._set_session(session) model = Sequential() model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu')) model.add(Dense(nb_class, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd') tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1) cbks = [tsb] model.fit(X_train, y_train, batch_size=batch_size, show_accuracy=True, validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2) assert os.path.exists(filepath) shutil.rmtree(filepath) # case 2 Sequential w accuracy with tf.Graph().as_default(): session = tf.Session('') KTF._set_session(session) model = Sequential() model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu')) model.add(Dense(nb_class, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='sgd') tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1) cbks = [tsb] model.fit(X_train, y_train, batch_size=batch_size, show_accuracy=True, validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=2) assert os.path.exists(filepath) shutil.rmtree(filepath) # case 3 Graph with tf.Graph().as_default(): session = tf.Session('') KTF._set_session(session) model = Graph() model.add_input(name='X_vars', input_shape=(input_dim, )) model.add_node(Dense(nb_hidden, activation="sigmoid"), name='Dense1', input='X_vars') model.add_node(Dense(nb_class, activation="softmax"), name='last_dense', input='Dense1') model.add_output(name='output', input='last_dense') model.compile(optimizer='sgd', loss={'output': 'mse'}) tsb = callbacks.TensorBoard(log_dir=filepath, histogram_freq=1) cbks = [tsb] model.fit({'X_vars': X_train, 'output': y_train}, batch_size=batch_size, validation_data={'X_vars': X_test, 'output': y_test}, callbacks=cbks, nb_epoch=2) assert os.path.exists(filepath) shutil.rmtree(filepath) KTF._set_session(old_session)
print ("Method = CNN from the paper 'Convolutional Neural Networks for Sentence Classification'") np.random.seed(0) nb_filter = embeddings_dim model = Graph() model.add_input(name='input', input_shape=(max_sent_len,), dtype=int) model.add_node(Embedding(max_features, embeddings_dim, input_length=max_sent_len, mask_zero=False, weights=[embedding_weights] ), name='embedding', input='input') model.add_node(Dropout(0.25), name='dropout_embedding', input='embedding') for n_gram in [3, 5, 7]: model.add_node(Convolution1D(nb_filter=nb_filter, filter_length=n_gram, border_mode='valid', activation='relu', subsample_length=1, input_dim=embeddings_dim, input_length=max_sent_len), name='conv_' + str(n_gram), input='dropout_embedding') model.add_node(MaxPooling1D(pool_length=max_sent_len - n_gram + 1), name='maxpool_' + str(n_gram), input='conv_' + str(n_gram)) model.add_node(Flatten(), name='flat_' + str(n_gram), input='maxpool_' + str(n_gram)) model.add_node(Dropout(0.25), name='dropout', inputs=['flat_' + str(n) for n in [3, 5, 7]]) model.add_node(Dense(1, input_dim=nb_filter * len([3, 5, 7])), name='dense', input='dropout') model.add_node(Activation('sigmoid'), name='sigmoid', input='dense') model.add_output(name='output', input='sigmoid') if num_classes == 2: model.compile(loss={'output': 'binary_crossentropy'}, optimizer='adam') else: model.compile(loss={'output': 'categorical_crossentropy'}, optimizer='adam') model.fit({'input': train_sequences, 'output': train_labels}, batch_size=32, nb_epoch=30) results = np.array(model.predict({'input': test_sequences}, batch_size=32)['output']) if num_classes != 2: results = results.argmax(axis=-1) else: results = (results > 0.5).astype('int32') print ("Accuracy = " + repr( sklearn.metrics.accuracy_score( test_labels , results ) )) print (sklearn.metrics.classification_report( test_labels , results )) print ("Method = Bidirectional LSTM") np.random.seed(0) model = Graph()