Python layers.MaxPooling1D方法代码示例

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [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 MaxPooling1D [as 别名]
def downsampling(inputs, pool_type='max'):
    """
        In addition, downsampling with stride 2 essentially doubles the effective coverage 
        (i.e., coverage in the original document) of the convolution kernel; 
        therefore, after going through downsampling L times, 
        associations among words within a distance in the order of 2L can be represented. 
        Thus, deep pyramid CNN is computationally efficient for representing long-range associations 
        and so more global information. 
        参考: https://github.com/zonetrooper32/VDCNN/blob/keras_version/vdcnn.py
    :param inputs: tensor,
    :param pool_type: str, select 'max', 'k-max' or 'conv'
    :return: tensor,
    """
    if pool_type == 'max':
        output = MaxPooling1D(pool_size=3, strides=2, padding='SAME')(inputs)
    elif pool_type == 'k-max':
        output = k_max_pooling(top_k=int(K.int_shape(inputs)[1]/2))(inputs)
    elif pool_type == 'conv':
        output = Conv1D(kernel_size=3, strides=2, padding='SAME')(inputs)
    else:
        output = MaxPooling1D(pool_size=3, strides=2, padding='SAME')(inputs)
    return output 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def bidLstm_simple(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes):
    #inp = Input(shape=(maxlen, ))
    input_layer = Input(shape=(maxlen, embed_size), )
    #x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp)
    x = Bidirectional(LSTM(recurrent_units, return_sequences=True, dropout=dropout_rate,
                           recurrent_dropout=dropout_rate))(input_layer)
    x = Dropout(dropout_rate)(x)
    x_a = GlobalMaxPool1D()(x)
    x_b = GlobalAveragePooling1D()(x)
    #x_c = AttentionWeightedAverage()(x)
    #x_a = MaxPooling1D(pool_size=2)(x)
    #x_b = AveragePooling1D(pool_size=2)(x)
    x = concatenate([x_a,x_b])
    x = Dense(dense_size, activation="relu")(x)
    x = Dropout(dropout_rate)(x)
    x = Dense(nb_classes, activation="sigmoid")(x)
    model = Model(inputs=input_layer, outputs=x)
    model.summary()
    model.compile(loss='binary_crossentropy', 
        optimizer='adam', 
        metrics=['accuracy'])
    return model


# bidirectional LSTM with attention layer 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def cnn(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes):
    #inp = Input(shape=(maxlen, ))
    input_layer = Input(shape=(maxlen, embed_size), )
    #x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp)
    x = Dropout(dropout_rate)(input_layer) 
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    x = MaxPooling1D(pool_size=2)(x)
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    x = MaxPooling1D(pool_size=2)(x)
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    x = MaxPooling1D(pool_size=2)(x)
    x = GRU(recurrent_units)(x)
    x = Dropout(dropout_rate)(x)
    x = Dense(dense_size, activation="relu")(x)
    x = Dense(nb_classes, activation="sigmoid")(x)
    model = Model(inputs=input_layer, outputs=x)
    model.summary()  
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def cnn2_best(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes):
    #inp = Input(shape=(maxlen, ))
    input_layer = Input(shape=(maxlen, embed_size), )
    #x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp)
    x = Dropout(dropout_rate)(input_layer) 
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    #x = MaxPooling1D(pool_size=2)(x)
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    #x = MaxPooling1D(pool_size=2)(x)
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    #x = MaxPooling1D(pool_size=2)(x)
    x = GRU(recurrent_units, return_sequences=False, dropout=dropout_rate,
                           recurrent_dropout=dropout_rate)(x)
    #x = Dropout(dropout_rate)(x)
    x = Dense(dense_size, activation="relu")(x)
    x = Dense(nb_classes, activation="sigmoid")(x)
    model = Model(inputs=input_layer, outputs=x)
    model.summary()  
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def cnn2(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes):
    #inp = Input(shape=(maxlen, ))
    input_layer = Input(shape=(maxlen, embed_size), )
    #x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp)
    x = Dropout(dropout_rate)(input_layer) 
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    #x = MaxPooling1D(pool_size=2)(x)
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    #x = MaxPooling1D(pool_size=2)(x)
    x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
    #x = MaxPooling1D(pool_size=2)(x)
    x = GRU(recurrent_units, return_sequences=False, dropout=dropout_rate,
                           recurrent_dropout=dropout_rate)(x)
    #x = Dropout(dropout_rate)(x)
    x = Dense(dense_size, activation="relu")(x)
    x = Dense(nb_classes, activation="sigmoid")(x)
    model = Model(inputs=input_layer, outputs=x)
    model.summary()  
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def conv(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes):
    filter_kernels = [7, 7, 5, 5, 3, 3]
    #inp = Input(shape=(maxlen, ))
    input_layer = Input(shape=(maxlen, embed_size), )
    #x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp)
    conv = Conv1D(nb_filter=recurrent_units, filter_length=filter_kernels[0], border_mode='valid', activation='relu')(input_layer)
    conv = MaxPooling1D(pool_length=3)(conv)
    conv1 = Conv1D(nb_filter=recurrent_units, filter_length=filter_kernels[1], border_mode='valid', activation='relu')(conv)
    conv1 = MaxPooling1D(pool_length=3)(conv1)
    conv2 = Conv1D(nb_filter=recurrent_units, filter_length=filter_kernels[2], border_mode='valid', activation='relu')(conv1)
    conv3 = Conv1D(nb_filter=recurrent_units, filter_length=filter_kernels[3], border_mode='valid', activation='relu')(conv2)
    conv4 = Conv1D(nb_filter=recurrent_units, filter_length=filter_kernels[4], border_mode='valid', activation='relu')(conv3)
    conv5 = Conv1D(nb_filter=recurrent_units, filter_length=filter_kernels[5], border_mode='valid', activation='relu')(conv4)
    conv5 = MaxPooling1D(pool_length=3)(conv5)
    conv5 = Flatten()(conv5)
    z = Dropout(0.5)(Dense(dense_size, activation='relu')(conv5))
    #x = GlobalMaxPool1D()(x)
    x = Dense(nb_classes, activation="sigmoid")(z)
    model = Model(inputs=input_layer, outputs=x)
    model.summary()  
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model


# LSTM + conv 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def byte_block(in_layer, nb_filter=(64, 100), filter_length=(3, 3), subsample=(2, 1), pool_length=(2, 2)):
    block = in_layer
    for i in range(len(nb_filter)):
        block = Conv1D(filters=nb_filter[i],
                       kernel_size=filter_length[i],
                       padding='valid',
                       activation='tanh',
                       strides=subsample[i])(block)
        # block = BatchNormalization()(block)
        # block = Dropout(0.1)(block)
        if pool_length[i]:
            block = MaxPooling1D(pool_size=pool_length[i])(block)

    # block = Lambda(max_1d, output_shape=(nb_filter[-1],))(block)
    block = GlobalMaxPool1D()(block)
    block = Dense(128, activation='relu')(block)
    return block 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def __call__(self, inputs):
        x = inputs[0]

        kernel_regularizer = kr.L1L2(self.l1_decay, self.l2_decay)
        x = kl.Conv1D(128, 11,
                      kernel_initializer=self.init,
                      kernel_regularizer=kernel_regularizer)(x)
        x = kl.Activation('relu')(x)
        x = kl.MaxPooling1D(4)(x)

        x = kl.Flatten()(x)

        kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
        x = kl.Dense(self.nb_hidden,
                     kernel_initializer=self.init,
                     kernel_regularizer=kernel_regularizer)(x)
        x = kl.Activation('relu')(x)
        x = kl.Dropout(self.dropout)(x)

        return self._build(inputs, x) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def build_model(vocab_size, embedding_dim, sequence_length, embedding_matrix):

    sequence_input = Input(shape=(sequence_length,), dtype='int32')
    embedding_layer = Embedding(input_dim=vocab_size,
                                output_dim=embedding_dim,
                                weights=[embedding_matrix],
                                input_length=sequence_length,
                                trainable=False,
                                name="embedding")(sequence_input)
    x = Conv1D(128, 5, activation='relu')(embedding_layer)
    x = MaxPooling1D(5)(x)
    x = Conv1D(128, 5, activation='relu')(x)
    x = MaxPooling1D(5)(x)
    x = Conv1D(128, 5, activation='relu')(x)
    x = GlobalMaxPooling1D()(x)
    x = Dense(128, activation='relu')(x)
    preds = Dense(20, activation='softmax')(x)

    model = Model(sequence_input, preds)
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def build_model(vocab_size, embedding_dim, sequence_length):
    sequence_input = Input(shape=(sequence_length,), dtype='int32')
    embedding_layer = Embedding(input_dim=vocab_size,
                                output_dim=embedding_dim,
                                input_length=sequence_length,
                                name="embedding")(sequence_input)
    x = Conv1D(128, 5, activation='relu')(embedding_layer)
    x = MaxPooling1D(5)(x)
    x = Conv1D(128, 5, activation='relu')(x)
    x = MaxPooling1D(5)(x)
    x = Conv1D(128, 5, activation='relu')(x)
    x = GlobalMaxPooling1D()(x)
    x = Dense(128, activation='relu')(x)
    preds = Dense(20, activation='softmax')(x)

    model = Model(sequence_input, preds)
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def wdcnn(filters, kernerl_size, strides, conv_padding, pool_padding,  pool_size, BatchNormal):
    """wdcnn层神经元

    :param filters: 卷积核的数目，整数
    :param kernerl_size: 卷积核的尺寸，整数
    :param strides: 步长，整数
    :param conv_padding: 'same','valid'
    :param pool_padding: 'same','valid'
    :param pool_size: 池化层核尺寸，整数
    :param BatchNormal: 是否Batchnormal，布尔值
    :return: model
    """
    model.add(Conv1D(filters=filters, kernel_size=kernerl_size, strides=strides,
                     padding=conv_padding, kernel_regularizer=l2(1e-4)))
    if BatchNormal:
        model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPooling1D(pool_size=pool_size, padding=pool_padding))
    return model

# 实例化序贯模型 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def __init__(self):
        from keras.preprocessing import sequence
        from keras.models import load_model
        from keras.models import Sequential
        from keras.preprocessing import sequence
        from keras.layers import Dense, Dropout, Activation, Lambda, Input, merge, Flatten
        from keras.layers import Embedding
        from keras.layers import Convolution1D, MaxPooling1D
        from keras import backend as K
        from keras.models import Model
        from keras.regularizers import l2
        global sequence, load_model, Sequential, Dense, Dropout, Activation, Lambda, Input, merge, Flatten
        global Embedding, Convolution1D, MaxPooling1D, K, Model, l2
        self.svm_clf = MiniClassifier(os.path.join(robotreviewer.DATA_ROOT, 'rct/rct_svm_weights.npz'))
        cnn_weight_files = glob.glob(os.path.join(robotreviewer.DATA_ROOT, 'rct/*.h5'))
        self.cnn_clfs = [load_model(cnn_weight_file) for cnn_weight_file in cnn_weight_files]
        self.svm_vectorizer = HashingVectorizer(binary=False, ngram_range=(1, 1), stop_words='english')
        self.cnn_vectorizer = KerasVectorizer(vocab_map_file=os.path.join(robotreviewer.DATA_ROOT, 'rct/cnn_vocab_map.pck'), stop_words='english')
        with open(os.path.join(robotreviewer.DATA_ROOT, 'rct/rct_model_calibration.json'), 'r') as f:
            self.constants = json.load(f)

        self.calibration_lr = {}
        with open(os.path.join(robotreviewer.DATA_ROOT, 'rct/svm_cnn_ptyp_calibration.pck'), 'rb') as f:
            self.calibration_lr['svm_cnn_ptyp'] = pickle.load(f)

        with open(os.path.join(robotreviewer.DATA_ROOT, 'rct/svm_cnn_calibration.pck'), 'rb') as f:
            self.calibration_lr['svm_cnn'] = pickle.load(f) 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def cnn_model(max_len=400,
              vocabulary_size=20000,
              embedding_dim=128,
              hidden_dim=128,
              num_filters=512,
              filter_sizes="3,4,5",
              num_classses=4,
              dropout=0.5):
    print("Creating text CNN Model...")
    # a tensor
    inputs = Input(shape=(max_len,), dtype='int32')
    # emb
    embedding = Embedding(input_dim=vocabulary_size, output_dim=embedding_dim,
                          input_length=max_len, name="embedding")(inputs)
    # convolution block
    if "," in filter_sizes:
        filter_sizes = filter_sizes.split(",")
    else:
        filter_sizes = [3, 4, 5]
    conv_blocks = []
    for sz in filter_sizes:
        conv = Convolution1D(filters=num_filters,
                             kernel_size=int(sz),
                             strides=1,
                             padding='valid',
                             activation='relu')(embedding)
        conv = MaxPooling1D()(conv)
        conv = Flatten()(conv)
        conv_blocks.append(conv)
    conv_concate = Concatenate()(conv_blocks) if len(conv_blocks) > 1 else conv_blocks[0]
    dropout_layer = Dropout(dropout)(conv_concate)
    output = Dense(hidden_dim, activation='relu')(dropout_layer)
    output = Dense(num_classses, activation='softmax')(output)
    # model
    model = Model(inputs=inputs, outputs=output)
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    model.summary()
    return model 

# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import MaxPooling1D [as 别名]
def conv1d(max_len, embed_size):
    '''
    CNN without Batch Normalisation.
    :param max_len: maximum sentence numbers, default=200
    :param embed_size: ELMo embeddings dimension, default=1024
    :return: CNN without BN model
    '''
    filter_sizes = [2, 3, 4, 5, 6]
    num_filters = 128
    drop = 0.5
    inputs = Input(shape=(max_len,embed_size), dtype='float32')

    conv_0 = Conv1D(num_filters, kernel_size=(filter_sizes[0]))(inputs)
    act_0 = Activation('relu')(conv_0)
    conv_1 = Conv1D(num_filters, kernel_size=(filter_sizes[1]))(inputs)
    act_1 = Activation('relu')(conv_1)
    conv_2 = Conv1D(num_filters, kernel_size=(filter_sizes[2]))(inputs)
    act_2 = Activation('relu')(conv_2)
    conv_3 = Conv1D(num_filters, kernel_size=(filter_sizes[3]))(inputs)
    act_3 = Activation('relu')(conv_3)
    conv_4 = Conv1D(num_filters, kernel_size=(filter_sizes[4]))(inputs)
    act_4 = Activation('relu')(conv_4)

    maxpool_0 = MaxPooling1D(pool_size=(max_len - filter_sizes[0]))(act_0)
    maxpool_1 = MaxPooling1D(pool_size=(max_len - filter_sizes[1]))(act_1)
    maxpool_2 = MaxPooling1D(pool_size=(max_len - filter_sizes[2]))(act_2)
    maxpool_3 = MaxPooling1D(pool_size=(max_len - filter_sizes[3]))(act_3)
    maxpool_4 = MaxPooling1D(pool_size=(max_len - filter_sizes[4]))(act_4)

    concatenated_tensor = Concatenate()([maxpool_0, maxpool_1, maxpool_2, maxpool_3, maxpool_4])
    flatten = Flatten()(concatenated_tensor)
    dropout = Dropout(drop)(flatten)
    output = Dense(units=1, activation='sigmoid')(dropout)

    model = Model(inputs=inputs, outputs=output)
    #model = multi_gpu_model(model, gpus=gpus)
    model.summary()
    model.compile(loss='binary_crossentropy', metrics=['acc'], optimizer='adam')
    return model