def testKerasModel(self): model = Sequential( [Dense(10, input_shape=(100,)), Activation('relu', name='my_relu')]) summary = self.keras_model(name='my_name', data=model, step=1) first_val = summary.value[0] self.assertEqual(model.to_json(), first_val.tensor.string_val[0])
def fit(x_train, y_train, plot=True): #Data preprocessing if plot: display_data(x_train, y_train) x_train = x_train / 255.0 x_train = x_train.reshape([-1, 784]) y_train = one_hot(y_train) model = Sequential([ Dense(512, input_dim=784), Activation('relu'), Dense(512), Activation('relu'), Dense(512), Activation('relu'), Dense(10), Activation('softmax') ]) optimiser = keras.optimizers.Adam(learning_rate=0.01) model.compile( loss='categorical_crossentropy', #Loss function for one hot inputs optimizer=optimiser, metrics=['accuracy'], ) model.fit(x_train, y_train, validation_split=0.2, batch_size=64, epochs=20) return model
def testKerasModel_usesDefaultStep(self): model = Sequential( [Dense(10, input_shape=(100,)), Activation('relu', name='my_relu')]) try: summary_ops.set_step(42) event = self.keras_model(name='my_name', data=model) self.assertEqual(42, event.step) finally: # Reset to default state for other tests. summary_ops.set_step(None)
def testKerasModel_otherExceptions(self): model = Sequential() with test.mock.patch.object(model, 'to_json') as mock_to_json: with test.mock.patch.object(logging, 'warn') as mock_log: mock_to_json.side_effect = Exception('oops') self.assertFalse( summary_ops.keras_model(name='my_name', data=model, step=1)) self.assertRegex( str(mock_log.call_args), 'Model failed to serialize as JSON. Ignoring... oops')
def __init__(self): self.model = Sequential([ Dense(40, input_shape=(4, ), activation="relu"), Dense(40, activation="relu"), Dense(40, activation="relu"), Dense(4, activation="tanh") ]) # TODO: xavier initialization? self.model.compile(loss=keras.losses.mean_squared_error, optimizer=keras.optimizers.Adam(lr=0.001)) self.memory = deque(maxlen=10000)
def seq_lstm(self, y_cols_idx=[-1]): from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense, LSTM, Dropout len_outputs = len(self.y_cols) len_features = len(self.x_cols) regressior = Sequential() regressior.add( LSTM( units=60, activation="relu", return_sequences=True, input_shape=(self.x_train.shape[1], len_features), ) ) regressior.add(Dropout(0.2)) regressior.add(LSTM(units=120, activation="relu", return_sequences=True)) regressior.add(Dropout(0.2)) regressior.add(LSTM(units=240, activation="relu", return_sequences=True)) regressior.add(Dropout(0.2)) regressior.add(LSTM(units=240, activation="relu", return_sequences=True)) regressior.add(Dropout(0.2)) regressior.add(LSTM(units=120, activation="tanh")) regressior.add(Dropout(0.2)) regressior.add(Dense(units=len_outputs)) self.model = regressior # self.save("my_model") # print(self.model.summary()) # print("model done") return regressior pass
def __init__(self, input_space, action_space): #HyperParameters self.GAMMA = 0.95 self.LEARNING_RATE = 0.002 self.MEMORY_SIZE = 1000000 self.BATCH_SIZE = 30 self.EXPLORATION_MAX = 1.0 self.EXPLORATION_MIN = 0.01 self.EXPLORATION_DECAY = 0.997 self.exploration_rate = self.EXPLORATION_MAX self.reward = 0 self.actions = action_space #Experience Replay self.memory = deque(maxlen=self.MEMORY_SIZE) #Create the NN model self.model = Sequential() self.model.add( Dense(64, input_shape=(input_space, ), activation="relu")) self.model.add(Dense(64, activation="relu")) self.model.add(Dense(self.actions, activation="softmax")) self.model.compile(loss="mse", optimizer=Adam(lr=self.LEARNING_RATE))
from tensorflow.python.keras.layers import LSTM, RepeatVector, TimeDistributed, Dense from src.helpers.helper_classifier import generate_data, invert n_samples = 200000 n_numbers = 2 largest = 10000 alphabet = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '-', '+', ' '] n_chars = len(alphabet) n_in_seq_length = n_numbers * ceil(log10(largest + 1)) + n_numbers - 1 n_out_seq_length = ceil(log10(n_numbers * (largest + 1))) n_batch = 100 n_epoch = 500 model = Sequential([ LSTM(100, input_shape=(n_in_seq_length, n_chars)), RepeatVector(n_out_seq_length), LSTM(50, return_sequences=True), TimeDistributed(Dense(n_chars, activation='softmax')) ]) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) for i in range(n_epoch): x, y = generate_data(n_samples, largest, alphabet) model.fit(x, y, epochs=1, batch_size=n_batch) model.save('training/keras_classifier.h5') # evaluate on some new patterns x, y = generate_data(n_samples, largest, alphabet) result = model.predict(x, batch_size=n_batch, verbose=0)
print('단어 카운트:', token.word_counts) print('문장 카운트:', token.document_count) print('각 단어가 몇개의 문장에 포함되어 있는가 :', token.word_docs) print('각 단어에 매겨진 인덱스 값 :', token.word_index) print() # 텍스트를 읽고 긍정 , 부정 분류 예측 docs = ['너무 재밌네요', '최고에요','참 잘만든 영화예요','추천하고 싶은 영화네요','한번 더 보고싶네요', '글쎄요','별로네요','생각보다 지루합니다','연기가 좋지않아요','재미없어요'] import numpy as np classes = np.array([1,1,1,1,1,0,0,0,0,0]) token = Tokenizer() token.fit_on_texts(docs) print(token.word_index) model = Sequential() model.add(Embedding(word_size,8,input_length=4)) #model.add(Flatten()) model.add(LSTM(32)) model.add(Dense(1,activation='sigmoid')) print(model.summary()) model.compile(optimizer='adam',loss='binary_crossentropy')
smooth_predictions = [] for row in y_pred: smooth_predictions.append(np.argmax(row)) return smooth_predictions if __name__ == '__main__': x_train, x_test, y_train, y_test = get_data() #Sequential model used since each layer has exactly one input and one output tensor model = Sequential([ Dense(512, input_dim=784), # Activation('relu'), #Activation function Dense(512), #512 output neurons Activation( 'relu' ), #Replaces negative values with zero and keeps positive values Dense(512), Activation('relu'), Dense(10), Activation('softmax') #Normalisation ]) #Could change this optimiser #Adam optimisation is a stochastic gradient descent method #Can handle sparse gradients on noisy problems optimiser = keras.optimizers.Adam(learning_rate=0.01) #Training configuration model.compile( loss='categorical_crossentropy', #Loss function for one hot inputs optimizer=optimiser,
def build_model(use_gpu: bool = False, num_units: int = 64, num_layers: int = 1, dropout_rate: float = 0.0, batch_size: int = 1000, window_size: int = 10, num_params: int = 0): """ Builds the RNN-Model for character prediction. :param window_size: Sequence size :param batch_size: {int} Size of batch :param dropout_rate: {float} Regulating Dropout rate between layers :param num_layers: {int} Number of layers to build :param num_units: {int} Number of LSTM-Units to use in network :param use_gpu: {bool} Uses Tensorflow GPU support if True, otherwise trains on CPU :param num_params: {int} Number of control parameters :return: Keras model """ # Load max 5000 entries from the dataset to build the Tokenizer / vocabulary loader = Loader(min(batch_size, 5000), 0) tokenizer = Tokenizer(filters='', split='°', lower=False) for dataframe in loader: chars = set() for name in dataframe['name']: chars.update(set(str(name))) tokenizer.fit_on_texts(list(chars)) tokenizer.fit_on_texts(['pre', '<end>', 'pad']) # Build Keras Model model = Sequential() for r in range(0, max(num_layers - 1, 0)): model.add(layer=(CuDNNLSTM if use_gpu else LSTM )(num_units, input_shape=(window_size, len(tokenizer.index_word) + 1 + num_params), return_sequences=True)) model.add(Dropout(dropout_rate)) model.add( layer=(CuDNNLSTM if use_gpu else LSTM)(num_units, input_shape=( window_size, len(tokenizer.index_word) + 1 + num_params))) model.add(Dense(len(tokenizer.index_word) + 1, activation='softmax')) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # Show summary print(model.summary()) return model, tokenizer
def build_model(): """ Function that build the CNN + LSTM network """ with tf.name_scope('CNN_LSTM'): model = Sequential() with tf.name_scope('Conv1'): model.add( TimeDistributed(Convolution2D(16, (5, 5), padding='same', strides=(2, 2)), input_shape=(15, 16, 3200, 1), name='Conv1')) model.add(BatchNormalization()) model.add(Activation('relu')) with tf.name_scope('Conv2'): model.add( TimeDistributed( Convolution2D(32, (5, 5), padding='same', strides=(1, 1), name='Conv2'))) model.add(Activation('relu')) with tf.name_scope('Pooling'): model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2)))) with tf.name_scope('Conv3'): model.add( TimeDistributed( Convolution2D(32, (5, 5), padding='same', strides=(1, 1), name='Conv3'))) model.add(Activation('relu')) with tf.name_scope('Conv4'): model.add( TimeDistributed( Convolution2D(32, (5, 5), padding='same', strides=(1, 1), name='Conv4'))) model.add(Activation('relu')) with tf.name_scope('Pooling'): model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2)))) with tf.name_scope('FC1'): model.add(TimeDistributed(Flatten(), name='FC1')) model.add(Activation('relu')) model.add(TimeDistributed(Dropout(0.25))) with tf.name_scope('FC2'): model.add(TimeDistributed(Dense(256), name='FC2')) model.add(Activation('relu')) model.add(TimeDistributed(Dropout(0.25))) with tf.name_scope('LSTM'): model.add(tf.keras.layers.CuDNNLSTM(64, return_sequences=False)) model.add(Dropout(0.5)) with tf.name_scope('OutputLayer'): model.add(Dense(2, activation='softmax')) with tf.name_scope('Optimizer'): optimizer = optimizers.adam(lr=1e-4, decay=1e-5) with tf.name_scope('Loss'): model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model