自学教程：Python layers.LSTM属性代码示例

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def create_model(time_window_size, metric):        model = Sequential()        model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu',                         input_shape=(time_window_size, 1)))        model.add(MaxPooling1D(pool_size=4))        model.add(LSTM(64))        model.add(Dense(units=time_window_size, activation='linear'))        model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])        # model.compile(optimizer='adam', loss='mean_squared_error', metrics=[metric])        # model.compile(optimizer="sgd", loss="mse", metrics=[metric])        print(model.summary())        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def get_model_41(params):    embedding_weights = pickle.load(open("../data/datasets/train_data/embedding_weights_w2v-google_MSD-AG.pk","rb"))    # main sequential model    model = Sequential()    model.add(Embedding(len(embedding_weights[0]), params['embedding_dim'], input_length=params['sequence_length'],                        weights=embedding_weights))    #model.add(Dropout(params['dropout_prob'][0], input_shape=(params['sequence_length'], params['embedding_dim'])))    model.add(LSTM(2048))    #model.add(Dropout(params['dropout_prob'][1]))    model.add(Dense(output_dim=params["n_out"], init="uniform"))    model.add(Activation(params['final_activation']))    logging.debug("Output CNN: %s" % str(model.output_shape))    if params['final_activation'] == 'linear':        model.add(Lambda(lambda x :K.l2_normalize(x, axis=1)))    return model# CRNN Arch for audio 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def create_model():    inputs = Input(shape=(length,), dtype='int32', name='inputs')    embedding_1 = Embedding(len(vocab), EMBED_DIM, input_length=length, mask_zero=True)(inputs)    bilstm = Bidirectional(LSTM(EMBED_DIM // 2, return_sequences=True))(embedding_1)    bilstm_dropout = Dropout(DROPOUT_RATE)(bilstm)    embedding_2 = Embedding(len(vocab), EMBED_DIM, input_length=length)(inputs)    con = Conv1D(filters=FILTERS, kernel_size=2 * HALF_WIN_SIZE + 1, padding='same')(embedding_2)    con_d = Dropout(DROPOUT_RATE)(con)    dense_con = TimeDistributed(Dense(DENSE_DIM))(con_d)    rnn_cnn = concatenate([bilstm_dropout, dense_con], axis=2)    dense = TimeDistributed(Dense(len(chunk_tags)))(rnn_cnn)    crf = CRF(len(chunk_tags), sparse_target=True)    crf_output = crf(dense)    model = Model(input=[inputs], output=[crf_output])    model.compile(loss=crf.loss_function, optimizer=Adam(), metrics=[crf.accuracy])    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def get_audio_model(self):		# Modality specific hyperparameters		self.epochs = 100		self.batch_size = 50		# Modality specific parameters		self.embedding_dim = self.train_x.shape[2]		print("Creating Model...")				inputs = Input(shape=(self.sequence_length, self.embedding_dim), dtype='float32')		masked = Masking(mask_value =0)(inputs)		lstm = Bidirectional(LSTM(300, activation='tanh', return_sequences = True, dropout=0.4))(masked)		lstm = Bidirectional(LSTM(300, activation='tanh', return_sequences = True, dropout=0.4), name="utter")(lstm)		output = TimeDistributed(Dense(self.classes,activation='softmax'))(lstm)		model = Model(inputs, output)		return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def get_bimodal_model(self):		# Modality specific hyperparameters		self.epochs = 100		self.batch_size = 10		# Modality specific parameters		self.embedding_dim = self.train_x.shape[2]		print("Creating Model...")				inputs = Input(shape=(self.sequence_length, self.embedding_dim), dtype='float32')		masked = Masking(mask_value =0)(inputs)		lstm = Bidirectional(LSTM(300, activation='tanh', return_sequences = True, dropout=0.4), name="utter")(masked)		output = TimeDistributed(Dense(self.classes,activation='softmax'))(lstm)		model = Model(inputs, output)		return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def _build(self):        # the model that will be trained        rnn_x = Input(shape=(None, Z_DIM + ACTION_DIM))        lstm = LSTM(HIDDEN_UNITS, return_sequences=True, return_state=True)        lstm_output, _, _ = lstm(rnn_x)        mdn = Dense(Z_DIM)(lstm_output)        rnn = Model(rnn_x, mdn)        # the model used during prediction        state_input_h = Input(shape=(HIDDEN_UNITS,))        state_input_c = Input(shape=(HIDDEN_UNITS,))        state_inputs = [state_input_h, state_input_c]                _, state_h, state_c = lstm(rnn_x, initial_state=state_inputs)        forward = Model([rnn_x] + state_inputs, [state_h, state_c])        optimizer = Adam(lr=0.0001)        # optimizer = SGD(lr=0.0001, decay=1e-4, momentum=0.9, nesterov=True)        rnn.compile(loss='mean_squared_error', optimizer=optimizer)        return [rnn, forward] 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def _build_model(self, num_features, num_actions, max_history_len):        """Build a keras model and return a compiled model.        :param max_history_len: The maximum number of historical turns used to                                decide on next action"""        from keras.layers import LSTM, Activation, Masking, Dense        from keras.models import Sequential        n_hidden = 32  # size of hidden layer in LSTM        # Build Model        batch_shape = (None, max_history_len, num_features)        model = Sequential()        model.add(Masking(-1, batch_input_shape=batch_shape))        model.add(LSTM(n_hidden, batch_input_shape=batch_shape))        model.add(Dense(input_dim=n_hidden, output_dim=num_actions))        model.add(Activation('softmax'))        model.compile(loss='categorical_crossentropy',                      optimizer='adam',                      metrics=['accuracy'])        logger.debug(model.summary())        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def _build_model(self, num_features, num_actions, max_history_len):        """Build a keras model and return a compiled model.        :param max_history_len: The maximum number of historical                                turns used to decide on next action        """        from keras.layers import LSTM, Activation, Masking, Dense        from keras.models import Sequential        n_hidden = 32  # Neural Net and training params        batch_shape = (None, max_history_len, num_features)        # Build Model        model = Sequential()        model.add(Masking(-1, batch_input_shape=batch_shape))        model.add(LSTM(n_hidden, batch_input_shape=batch_shape))        model.add(Dense(input_dim=n_hidden, units=num_actions))        model.add(Activation('softmax'))        model.compile(loss='categorical_crossentropy',                      optimizer='rmsprop',                      metrics=['accuracy'])        logger.debug(model.summary())        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def GeneratorPretraining(V, E, H):    '''    Model for Generator pretraining. This model's weights should be shared with        Generator.    # Arguments:        V: int, Vocabrary size        E: int, Embedding size        H: int, LSTM hidden size    # Returns:        generator_pretraining: keras Model            input: word ids, shape = (B, T)            output: word probability, shape = (B, T, V)    '''    # in comment, B means batch size, T means lengths of time steps.    input = Input(shape=(None,), dtype='int32', name='Input') # (B, T)    out = Embedding(V, E, mask_zero=True, name='Embedding')(input) # (B, T, E)    out = LSTM(H, return_sequences=True, name='LSTM')(out)  # (B, T, H)    out = TimeDistributed(        Dense(V, activation='softmax', name='DenseSoftmax'),        name='TimeDenseSoftmax')(out)    # (B, T, V)    generator_pretraining = Model(input, out)    return generator_pretraining 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def __init__(self, sess, B, V, E, H, lr=1e-3):        '''        # Arguments:            B: int, Batch size            V: int, Vocabrary size            E: int, Embedding size            H: int, LSTM hidden size        # Optional Arguments:            lr: float, learning rate, default is 0.001        '''        self.sess = sess        self.B = B        self.V = V        self.E = E        self.H = H        self.lr = lr        self._build_gragh()        self.reset_rnn_state() 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def Discriminator(V, E, H=64, dropout=0.1):    '''    Disciriminator model.    # Arguments:        V: int, Vocabrary size        E: int, Embedding size        H: int, LSTM hidden size        dropout: float    # Returns:        discriminator: keras model            input: word ids, shape = (B, T)            output: probability of true data or not, shape = (B, 1)    '''    input = Input(shape=(None,), dtype='int32', name='Input')   # (B, T)    out = Embedding(V, E, mask_zero=True, name='Embedding')(input)  # (B, T, E)    out = LSTM(H)(out)    out = Highway(out, num_layers=1)    out = Dropout(dropout, name='Dropout')(out)    out = Dense(1, activation='sigmoid', name='FC')(out)    discriminator = Model(input, out)    return discriminator 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def test_lstm(self):        x_train = np.random.random((100, 100, 100))        y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)        x_test = np.random.random((20, 100, 100))        y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)        sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)        model = Sequential()        model.add(LSTM(32, return_sequences=True, input_shape=(100, 100)))        model.add(Flatten())        model.add(Dense(10, activation='softmax'))        model.compile(loss='categorical_crossentropy', optimizer=sgd)        model.fit(x_train, y_train, batch_size=32, epochs=1)        model.evaluate(x_test, y_test, batch_size=32) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def create_network(network_input, n_vocab):    """ create the structure of the neural network """    model = Sequential()    model.add(LSTM(        512,        input_shape=(network_input.shape[1], network_input.shape[2]),        recurrent_dropout=0.3,        return_sequences=True    ))    model.add(LSTM(512, return_sequences=True, recurrent_dropout=0.3,))    model.add(LSTM(512))    model.add(BatchNorm())    model.add(Dropout(0.3))    model.add(Dense(256))    model.add(Activation('relu'))    model.add(BatchNorm())    model.add(Dropout(0.3))    model.add(Dense(n_vocab))    model.add(Activation('softmax'))    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def prepare_sequences(notes, pitchnames, n_vocab):    """ Prepare the sequences used by the Neural Network """    # map between notes and integers and back    note_to_int = dict((note, number) for number, note in enumerate(pitchnames))    sequence_length = 100    network_input = []    output = []    for i in range(0, len(notes) - sequence_length, 1):        sequence_in = notes[i:i + sequence_length]        sequence_out = notes[i + sequence_length]        network_input.append([note_to_int[char] for char in sequence_in])        output.append(note_to_int[sequence_out])    n_patterns = len(network_input)    # reshape the input into a format compatible with LSTM layers    normalized_input = numpy.reshape(network_input, (n_patterns, sequence_length, 1))    # normalize input    normalized_input = normalized_input / float(n_vocab)    return (network_input, normalized_input) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def create_network(network_input, n_vocab):    """ create the structure of the neural network """    model = Sequential()    model.add(LSTM(        512,        input_shape=(network_input.shape[1], network_input.shape[2]),        recurrent_dropout=0.3,        return_sequences=True    ))    model.add(LSTM(512, return_sequences=True, recurrent_dropout=0.3,))    model.add(LSTM(512))    model.add(BatchNorm())    model.add(Dropout(0.3))    model.add(Dense(256))    model.add(Activation('relu'))    model.add(BatchNorm())    model.add(Dropout(0.3))    model.add(Dense(n_vocab))    model.add(Activation('softmax'))    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')    # Load the weights to each node    model.load_weights('weights.hdf5')    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def __init__(self, config: ModelConfig) -> None:        self.x_input = Input((config.obs_len, config.max_n_peds, pxy_dim))        # y_input = Input((config.obs_len, config.max_n_peds, pxy_dim))        self.grid_input = Input(            (config.obs_len, config.max_n_peds, config.max_n_peds,             config.grid_side_squared))        self.zeros_input = Input(            (config.obs_len, config.max_n_peds, config.lstm_state_dim))        # Social LSTM layers        self.lstm_layer = LSTM(config.lstm_state_dim, return_state=True)        self.W_e_relu = Dense(config.emb_dim, activation="relu")        self.W_a_relu = Dense(config.emb_dim, activation="relu")        self.W_p = Dense(out_dim)        self._build_model(config) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def __middle_hidden_layer(self, return_sequences):		if self.current_params["layer_type"]  == "GRU":			layer = GRU(self.current_params["hidden_neurons"], 				return_sequences=return_sequences, 				kernel_initializer=self.current_params["kernel_initializer"], 				recurrent_initializer=self.current_params["recurrent_initializer"], 				recurrent_regularizer=self.__generate_regulariser(self.current_params["r_l1_reg"], self.current_params["r_l2_reg"]), 				bias_regularizer=self.__generate_regulariser(self.current_params["b_l1_reg"], self.current_params["b_l2_reg"]),				dropout=self.current_params["dropout"], 				recurrent_dropout=self.current_params["recurrent_dropout"]			)		else:			layer = LSTM(self.current_params["hidden_neurons"], 				return_sequences=return_sequences, 				kernel_initializer=self.current_params["kernel_initializer"], 				recurrent_initializer=self.current_params["recurrent_initializer"], 				recurrent_regularizer=self.__generate_regulariser(self.current_params["r_l1_reg"], self.current_params["r_l2_reg"]), 				bias_regularizer=self.__generate_regulariser(self.current_params["b_l1_reg"], self.current_params["b_l2_reg"]),				dropout=self.current_params["dropout"], 				recurrent_dropout=self.current_params["recurrent_dropout"]			)		return layer 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def __build_model(self):        model = Sequential()        embedding_layer = Embedding(input_dim=len(self.vocab) + 1,                                    output_dim=self.embedding_dim,                                    weights=[self.embedding_mat],                                    trainable=False)        model.add(embedding_layer)        bilstm_layer = Bidirectional(LSTM(units=256, return_sequences=True))        model.add(bilstm_layer)        model.add(TimeDistributed(Dense(256, activation="relu")))        crf_layer = CRF(units=len(self.tags), sparse_target=True)        model.add(crf_layer)        model.compile(optimizer="adam", loss=crf_loss, metrics=[crf_viterbi_accuracy])        model.summary()        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def get_training_data(raw_dict, char_to_index):	'''	'Generate data for training LSTM from raw data	'	'raw_dict: 		original data. struct: {'title':"",'strains':'zzppz$ppzzp$...','paragraphs':"12345$67890$..."}	'char_to_index: 	dictonary map char to index	'	'return:	'	X [input chars sequence]	'	Y [char label]	'''		data_X = []	data_Y = []	for poem in raw_dict:		n_chars = len(poem['paragraphs'])		for i in range(0,n_chars - seq_len,1):			s_out = poem['paragraphs'][i+seq_len]			# never output '$'			if(s_out == '$'):				continue			s_in = poem['paragraphs'][i:i+seq_len]			data_X.append([char_to_index[c] for c in s_in])			data_Y.append(char_to_index[s_out])	return data_X,data_Y 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def get_training_data2(raw_dict, char_to_index):	'''	'Generate data for training LSTM from raw data without considering $	'	'raw_dict: 		original data. struct: {'title':"",'strains':'zzppz$ppzzp$...','paragraphs':"12345$67890$..."}	'char_to_index: 	dictonary map char to index	'	'return:	'	X [input chars sequence]	'	Y [char label]	'''		data_X = []	data_Y = []	for poem in raw_dict:		context = poem['paragraphs']		context.replace('$','')		n_chars = len(context)		for i in range(0,n_chars - seq_len - 1,1):			s_out = context[i+seq_len - 1]			s_in = context[i:i+seq_len - 1]			data_X.append([char_to_index[c] for c in s_in])			data_Y.append(char_to_index[s_out])	return data_X,data_Y 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def train(X,Y,file,load_path):	# define model	model = Sequential()	model.add(LSTM(n_mmu, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))	model.add(Dropout(dropout))	model.add(LSTM(n_mmu, return_sequences=True))	model.add(Dropout(dropout))	if n_layer == 3:		model.add(LSTM(n_mmu))		model.add(Dropout(dropout))	model.add(Dense(Y.shape[1], activation='softmax'))	model.compile(loss='categorical_crossentropy', optimizer='adam')	model.save(file + "/model-{}-{}.h5".format(n_mmu,dropout))	# define the checkpoint	filepath=file + "/weights-improvement-{epoch:02d}-{loss:.4f}.hdf5"	checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')	callbacks_list = [checkpoint]	# loading	if load_path != "":		model.load_weights(load_path)	# training	model.fit(X, Y, epochs=epoch, batch_size=batch, callbacks=callbacks_list,validation_split = 0.1) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def model_keras(num_words=3000, num_units=128):    '''    生成RNN模型    :param num_words:词汇数量    :param num_units:词向量维度,lstm神经元数量默认一样    :return:    '''    data_input = Input(shape=[None])    embedding = Embedding(input_dim=num_words, output_dim=num_units, mask_zero=True)(data_input)    lstm = LSTM(units=num_units, return_sequences=True)(embedding)    x = LSTM(units=num_units, return_sequences=True)(lstm)    # keras好像不支持内部对y操作,不能像tensorflow那样用reshape    # x = Reshape(target_shape=[-1, num_units])(x)    outputs = Dense(units=num_words, activation='softmax')(x)    model = Model(inputs=data_input, outputs=outputs)    model.compile(loss='sparse_categorical_crossentropy',                  optimizer=optimizers.adam(lr=0.01),                  metrics=['accuracy'])    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def model_0(input_dim, output_dim):    """    Total params: 127,584    Trainable params: 127,584    Non-trainable params: 0    :param input_dim:    :param output_dim:    :return:    """    # build the model: a single LSTM    print('Build model...')    model = Sequential()    model.add(LSTM(128, input_shape=input_dim))    model.add(Dense(output_dim))    model.add(Activation('softmax'))    return model, 'model_0'# summery of result for model_1 (deep 2):## 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def model_2(input_dim, output_dim):    """    Total params: 259,168    Trainable params: 259,168    Non-trainable params: 0    :param input_dim:    :param output_dim:    :return:    """    model = Sequential()    # model.add(LSTM(128, input_shape=(maxlen, len(chars))))    model.add(LSTM(128, input_shape=input_dim, return_sequences=True, dropout=0.2, recurrent_dropout=0.1))    model.add(LSTM(128, input_shape=input_dim, return_sequences=False, dropout=0.2, recurrent_dropout=0.1))    # model.add(LSTM(128, activation='relu', dropout=0.2))    model.add(Dense(output_dim))    model.add(Activation('softmax'))    return model, 'model_2'# Summery of result for this model: 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def model_6(input_dim, output_dim):    model = Sequential()    model.add(LSTM(128, input_shape=input_dim, return_sequences=True, recurrent_dropout=0.1))    model.add(Dropout(0.3))    model.add(LSTM(128, input_shape=input_dim, return_sequences=False, recurrent_dropout=0.1))    model.add(Dropout(0.3))    model.add(Dense(output_dim))    model.add(Activation('softmax'))    return model, 'model_6'# ------------------------------------------------------------------------# Unidirectional LSTM (Many to One)## Summery of result for this model:# Try 3:# batch_size=128, lr=0.001# With step 1 and neuron size 128 was very bad. Set step=3 and neuron size=256 and step=3# With Adam Optimizer, Lr=0.001 and step=3. after 61 epoch is the bset model !!!# Change from RMSProp to Adam fix the learning process# 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import LSTM [as 别名]def AlternativeRNNModel(vocab_size, max_len, rnnConfig, model_type):	embedding_size = rnnConfig['embedding_size']	if model_type == 'inceptionv3':		# InceptionV3 outputs a 2048 dimensional vector for each image, which we'll feed to RNN Model		image_input = Input(shape=(2048,))	elif model_type == 'vgg16':		# VGG16 outputs a 4096 dimensional vector for each image, which we'll feed to RNN Model		image_input = Input(shape=(4096,))	image_model_1 = Dense(embedding_size, activation='relu')(image_input)	image_model = RepeatVector(max_len)(image_model_1)	caption_input = Input(shape=(max_len,))	# mask_zero: We zero pad inputs to the same length, the zero mask ignores those inputs. E.g. it is an efficiency.	caption_model_1 = Embedding(vocab_size, embedding_size, mask_zero=True)(caption_input)	# Since we are going to predict the next word using the previous words	# (length of previous words changes with every iteration over the caption), we have to set return_sequences = True.	caption_model_2 = LSTM(rnnConfig['LSTM_units'], return_sequences=True)(caption_model_1)	# caption_model = TimeDistributed(Dense(embedding_size, activation='relu'))(caption_model_2)	caption_model = TimeDistributed(Dense(embedding_size))(caption_model_2)	# Merging the models and creating a softmax classifier	final_model_1 = concatenate([image_model, caption_model])	# final_model_2 = LSTM(rnnConfig['LSTM_units'], return_sequences=False)(final_model_1)	final_model_2 = Bidirectional(LSTM(rnnConfig['LSTM_units'], return_sequences=False))(final_model_1)	# final_model_3 = Dense(rnnConfig['dense_units'], activation='relu')(final_model_2)	# final_model = Dense(vocab_size, activation='softmax')(final_model_3)	final_model = Dense(vocab_size, activation='softmax')(final_model_2)	model = Model(inputs=[image_input, caption_input], outputs=final_model)	model.compile(loss='categorical_crossentropy', optimizer='adam')	# model.compile(loss='categorical_crossentropy', optimizer='rmsprop')	return model