python类AveragePooling1D()的实例源码

def discriminator_model(model_name="discriminator"):
    disc_input = Input(shape=(400, 1), name="discriminator_input")
    aux_input = Input(shape=(47,), name="auxilary_input")

    # Conv Layer 1
    x = Conv1D(filters=100, kernel_size=13, padding='same')(disc_input)
    x = LeakyReLU(0.2)(x) # output shape is 100 x 400
    x = AveragePooling1D(pool_size=20)(x) # ouput shape is 100 x 20

    # Conv Layer 2
    x = Conv1D(filters=250, kernel_size=13, padding='same')(x)
    x = LeakyReLU(0.2)(x) # output shape is 250 x 20
    x = AveragePooling1D(pool_size=5)(x) # output shape is 250 x 4

    # Conv Layer 3
    x = Conv1D(filters=300, kernel_size=13, padding='same')(x)
    x = LeakyReLU(0.2)(x) # output shape is 300 x 4
    x = Flatten()(x) # output shape is 1200

    x = concatenate([x, aux_input], axis=-1) # shape is 1247

    # Dense Layer 1
    x = Dense(200)(x)
    x = LeakyReLU(0.2)(x) # output shape is 200

    # Dense Layer 2
    x = Dense(1)(x)
    x = Activation('sigmoid')(x)

    discriminator_model = Model(
        outputs=[x], inputs=[disc_input, aux_input], name=model_name)

    return discriminator_model

def test_averagepooling_1d():
    for stride in [1, 2]:
        layer_test(convolutional.AveragePooling1D,
                   kwargs={'stride': stride,
                           'border_mode': 'valid'},
                   input_shape=(3, 5, 4))

def build_hcnn_model(opts, vocab_size=0, maxnum=50, maxlen=50, embedd_dim=50, embedding_weights=None, verbose=False):

    N = maxnum
    L = maxlen

    logger.info("Model parameters: max_sentnum = %d, max_sentlen = %d, embedding dim = %s, nbfilters = %s, filter1_len = %s, filter2_len = %s, drop rate = %s, l2 = %s" % (N, L, embedd_dim,
        opts.nbfilters, opts.filter1_len, opts.filter2_len, opts.dropout, opts.l2_value))

    word_input = Input(shape=(N*L,), dtype='int32', name='word_input')
    x = Embedding(output_dim=embedd_dim, input_dim=vocab_size, input_length=N*L, weights=embedding_weights, name='x')(word_input)
    drop_x = Dropout(opts.dropout, name='drop_x')(x)

    resh_W = Reshape((N, L, embedd_dim), name='resh_W')(drop_x)

    z = TimeDistributed(Convolution1D(opts.nbfilters, opts.filter1_len, border_mode='valid'), name='z')(resh_W)

    avg_z = TimeDistributed(AveragePooling1D(pool_length=L-opts.filter1_len+1), name='avg_z')(z)    # shape= (N, 1, nbfilters)

    resh_z = Reshape((N, opts.nbfilters), name='resh_z')(avg_z)     # shape(N, nbfilters)

    hz = Convolution1D(opts.nbfilters, opts.filter2_len, border_mode='valid', name='hz')(resh_z)
    # avg_h = MeanOverTime(mask_zero=True, name='avg_h')(hz)

    avg_hz = GlobalAveragePooling1D(name='avg_hz')(hz)
    y = Dense(output_dim=1, activation='sigmoid', name='output')(avg_hz)

    model = Model(input=word_input, output=y)

    if verbose:
        model.summary()

    start_time = time.time()
    model.compile(loss='mse', optimizer='rmsprop')
    total_time = time.time() - start_time
    logger.info("Model compiled in %.4f s" % total_time)

    return model

def test_averagepooling_1d():
    for stride in [1, 2]:
        layer_test(convolutional.AveragePooling1D,
                   kwargs={'stride': stride,
                           'border_mode': 'valid'},
                   input_shape=(3, 5, 4))

def test_averagepooling_1d():
    for stride in [1, 2]:
        layer_test(convolutional.AveragePooling1D,
                   kwargs={'stride': stride,
                           'border_mode': 'valid'},
                   input_shape=(3, 5, 4))

def lstm_attention_combine_train(X_train_list,y_train,vocab_size):
    N=len(X_train_list)

    X_train_list = [sequence.pad_sequences(x_train, maxlen=MAX_LEN) for x_train in X_train_list]

    input_list=[]
    out_list=[]
    for i in range(N):
        input,out=get_embedding_input_output('f%d' %i,vocab_size)
        input_list.append(input)
        out_list.append(out)

    x = merge(out_list,mode='concat')

    lstm_out = LSTM(HIDDEN_SIZE, return_sequences=True)(x)

    x = lstm_out
    for i in range(10):
        att = TimeDistributed(Dense(1))(x)
        att = Flatten()(att)
        att = Activation(activation="softmax")(att)
        att = RepeatVector(HIDDEN_SIZE)(att)
        att = Permute((2,1))(att)
        x = att

    mer = merge([att, lstm_out], "mul")
    mer = merge([mer, out_list[-1]], 'mul')
    hid = AveragePooling1D(pool_length=2)(mer)
    hid = Flatten()(hid)

    #hid = merge([hid,out_list[-1]], mode='concat')

    main_loss = Dense(1, activation='sigmoid', name='main_output')(hid)

    model = Model(input=input_list, output=main_loss)

    model.compile(loss='binary_crossentropy', optimizer='rmsprop')
    model.fit(X_train_list, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS)

    return model

def discriminator_model(model_name="discriminator"):
    disc_input = Input(shape=(400, 1), name="discriminator_input")
    aux_input = Input(shape=(47,), name="auxilary_input")

    # Conv Layer 1
    x = Convolution1D(nb_filter=100,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(disc_input)
    x = LeakyReLU(0.2)(x) # output shape is 100 x 400
    x = AveragePooling1D(pool_length=20)(x) # ouput shape is 100 x 20

    # Conv Layer 2
    x = Convolution1D(nb_filter=250,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(x)
    x = LeakyReLU(0.2)(x) # output shape is 250 x 20
    x = AveragePooling1D(pool_length=5)(x) # output shape is 250 x 4

    # Conv Layer 3
    x = Convolution1D(nb_filter=300,
                      filter_length=13,
                      border_mode='same',
                      subsample_length=1)(x)
    x = LeakyReLU(0.2)(x) # output shape is 300 x 4
    x = Flatten()(x) # output shape is 1200

    x = merge([x, aux_input], mode="concat", concat_axis=-1) # shape is 1247

    # Dense Layer 1
    x = Dense(200)(x)
    x = LeakyReLU(0.2)(x) # output shape is 200

    # Dense Layer 2
    x = Dense(1)(x)
    #x = Activation('sigmoid')(x)
    x = Activation('linear')(x) # output shape is 1

    discriminator_model = Model(
        input=[disc_input, aux_input], output=[x], name=model_name)

    return discriminator_model

def lstm_memory_train(X_train_list,y_train,vocab_size):
    N=len(X_train_list)

    X_train_list = [sequence.pad_sequences(x_train, maxlen=MAX_LEN) for x_train in X_train_list]

    input_list=[]
    out_list=[]
    for i in range(N):
        input,out=get_embedding_input_output('f%d' %i,vocab_size)
        input_list.append(input)
        out_list.append(out)

    x = merge(out_list,mode='concat')

    lstm_out = LSTM(HIDDEN_SIZE, return_sequences=True)(x)

    lstm_share=GRU(HIDDEN_SIZE, return_sequences=True)

    x = lstm_out
    for i in range(2):
        att = TimeDistributed(Dense(1))(x)
        att = Flatten()(att)
        att = Activation(activation="softmax")(att)
        att = RepeatVector(HIDDEN_SIZE)(att)
        att = Permute((2,1))(att)

        mer = merge([att, lstm_out], "mul")
        mer = merge([mer, out_list[-1]], 'mul')

        z = merge([lstm_out,mer],'sum')
        z = lstm_share(z)
        x = z

    hid = AveragePooling1D(pool_length=2)(x)
    hid = Flatten()(hid)

    #hid = merge([hid,out_list[-1]], mode='concat')

    main_loss = Dense(1, activation='sigmoid', name='main_output')(hid)

    model = Model(input=input_list, output=main_loss)

    model.compile(loss='binary_crossentropy', optimizer='rmsprop')
    model.fit(X_train_list, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS)

    return model