layers import Embedding from keras.layers import LSTM model = Sequential() model.add(Embedding(max_, output_dim=256)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']...
model.add(tf.keras.layers.Dropout(0.5)) for _ in range(encoder_layers): rnn = tf.keras.layers.LSTM(2**6, return_sequences=True) if encoder_bidirectional: rnn = tf.keras.layers.Bidirectional(rnn) model.add(rnn) model.add(tf.keras.layers.Dense(2, activation='softmax')) return model d...
from keras.layers import Dense, Activation#模型搭建阶段model= Sequential() model.add(Dense(32, activation='relu', input_dim=100))# Dense(32) is a fully-connected layer with 32 hidden units.model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='rmsprop', loss='binary_crossentrop...
fromkeras.modelsimportSequentialfromkeras.layersimportDense,Activation# 建立模型model = Sequential()# 初始化model.add(Dense(32,activation='relu',input_dim=100))# Dense代表全连接,有32个全连接层,最后接relu,输入的是100维model.add(Dense(1,activation='sigmoid'))# 添加新的全连接层model.compile(optimi...
下面用keras中函数式API构建一个简单的LSTM多分类模型,模型具体结构如下: import keras from keras.models import Sequential from keras.layers import Input, Dense, Dropout, Embedding, LSTM from keras.models import Model from keras import backend as K ...
在使用keras时候会出现总是占满GPU显存的情况,可以通过重设backend的GPU占用情况来进行调节。 import tensorflow as tf from keras.backend.tensorflow_backend import set_session config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction =0.3set_session(tf.Session(config=config)) ...
importnumpyasnpimporttensorflowastfmodel=tf.keras.models.Sequential([tf.keras.layers.Input(shape=(28,28),name='input'),tf.keras.layers.LSTM(20,time_major=False,return_sequences=True),tf.keras.layers.Flatten(),tf.keras.layers.Dense(10,activation=tf.nn.softmax,name='output') ...
Short-Term Memory Networks Long Short-Term Memory Networks或LSTM是一种流行的强大的循环神经网络(即...
import tensorflow as tf from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Embedding, LSTM, Dense def create_model(enc_vocab_size, dec_vocab_size, embedding_dim=256, units=512): # Define the model architecture encoder_inputs = Input(shape=(None,), name='enc...
from keras.layers.recurrent import LSTM TRAINING AND VALIDATION FILES xTrain = np.random.rand(200,10) yTrain = np.random.rand(200,1) xVal = np.random.rand(100,10) yVal = np.random.rand(100,1) ADD 3RD DIMENSION TO DATA xTrain = xTrain.reshape(len(xTrain), 1, xTrain.shape[1]...