自学教程：Python layers.merge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 merge [as 别名]def build_encoder(self):        # Encoder        img = Input(shape=self.img_shape)        h = Flatten()(img)        h = Dense(512)(h)        h = LeakyReLU(alpha=0.2)(h)        h = Dense(512)(h)        h = LeakyReLU(alpha=0.2)(h)        mu = Dense(self.latent_dim)(h)        log_var = Dense(self.latent_dim)(h)        latent_repr = merge([mu, log_var],                mode=lambda p: p[0] + K.random_normal(K.shape(p[0])) * K.exp(p[1] / 2),                output_shape=lambda p: p[0])        return Model(img, latent_repr) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def get_model(num_users, num_items, latent_dim, regs=[0,0]):    # Input variables    user_input = Input(shape=(1,), dtype='int32', name = 'user_input')    item_input = Input(shape=(1,), dtype='int32', name = 'item_input')    MF_Embedding_User = Embedding(input_dim = num_users, output_dim = latent_dim, name = 'user_embedding',                                  init = init_normal, W_regularizer = l2(regs[0]), input_length=1)    MF_Embedding_Item = Embedding(input_dim = num_items, output_dim = latent_dim, name = 'item_embedding',                                  init = init_normal, W_regularizer = l2(regs[1]), input_length=1)           # Crucial to flatten an embedding vector!    user_latent = Flatten()(MF_Embedding_User(user_input))    item_latent = Flatten()(MF_Embedding_Item(item_input))        # Element-wise product of user and item embeddings     predict_vector = merge([user_latent, item_latent], mode = 'mul')        # Final prediction layer    #prediction = Lambda(lambda x: K.sigmoid(K.sum(x)), output_shape=(1,))(predict_vector)    prediction = Dense(1, activation='sigmoid', init='lecun_uniform', name = 'prediction')(predict_vector)        model = Model(input=[user_input, item_input],                 output=prediction)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def block_reduction_a(input):    if K.image_dim_ordering() == "th":        channel_axis = 1    else:        channel_axis = -1    branch_0 = conv2d_bn(input, 384, 3, 3, subsample=(2,2), border_mode='valid')    branch_1 = conv2d_bn(input, 192, 1, 1)    branch_1 = conv2d_bn(branch_1, 224, 3, 3)    branch_1 = conv2d_bn(branch_1, 256, 3, 3, subsample=(2,2), border_mode='valid')    branch_2 = MaxPooling2D((3,3), strides=(2,2), border_mode='valid')(input)    x = merge([branch_0, branch_1, branch_2], mode='concat', concat_axis=channel_axis)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def block_reduction_b(input):    if K.image_dim_ordering() == "th":        channel_axis = 1    else:        channel_axis = -1    branch_0 = conv2d_bn(input, 192, 1, 1)    branch_0 = conv2d_bn(branch_0, 192, 3, 3, subsample=(2, 2), border_mode='valid')    branch_1 = conv2d_bn(input, 256, 1, 1)    branch_1 = conv2d_bn(branch_1, 256, 1, 7)    branch_1 = conv2d_bn(branch_1, 320, 7, 1)    branch_1 = conv2d_bn(branch_1, 320, 3, 3, subsample=(2,2), border_mode='valid')    branch_2 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(input)    x = merge([branch_0, branch_1, branch_2], mode='concat', concat_axis=channel_axis)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def _build(self):        print('Building Graph ...')        inputs = Input(shape=(self.window_size, self.userTagIntent_vocab_size),                       name='tagIntent_input')        lstm_forward = LSTM(output_dim=self.hidden_size,                            return_sequences=False,                            name='LSTM_forward')(inputs)        lstm_forward = Dropout(self.dropout)(lstm_forward)        lstm_backward = LSTM(output_dim=self.hidden_size,                             return_sequences=False,                             go_backwards=True,                             name='LSTM_backward')(inputs)        lstm_backward = Dropout(self.dropout)(lstm_backward)        lstm_concat = merge([lstm_forward, lstm_backward],                            mode='concat', concat_axis=-1,                            name='merge_bidirections')        act_softmax = Dense(output_dim=self.agentAct_vocab_size,                            activation='sigmoid')(lstm_concat)        self.model = Model(input=inputs, output=act_softmax)        self.model.compile(optimizer=self.optimizer,                           loss='binary_crossentropy') 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def identity_block(input_tensor, kernel_size, filters, stage, block):    nb_filter1, nb_filter2, nb_filter3 = filters    bn_axis = 1    conv_name_base = 'res' + str(stage) + block + '_branch'    bn_name_base = 'bn' + str(stage) + block + '_branch'    x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)    x = Activation('relu')(x)    x = Convolution2D(nb_filter2, kernel_size, kernel_size,                      border_mode='same', name=conv_name_base + '2b')(x)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)    x = Activation('relu')(x)    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)    x = merge([x, input_tensor], mode='sum')    x = Activation('relu')(x)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def conv_block_atrous(input_tensor, kernel_size, filters, stage, block, atrous_rate=(2, 2)):    nb_filter1, nb_filter2, nb_filter3 = filters    bn_axis = 1    conv_name_base = 'res' + str(stage) + block + '_branch'    bn_name_base = 'bn' + str(stage) + block + '_branch'    x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)    x = Activation('relu')(x)    x = AtrousConvolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same',                            atrous_rate=atrous_rate, name=conv_name_base + '2b')(x)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)    x = Activation('relu')(x)    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)    shortcut = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '1')(input_tensor)    shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)    x = merge([x, shortcut], mode='sum')    x = Activation('relu')(x)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def downsample_block(x, nb_channels, kernel_size=3, bottleneck=True,                     l2_reg=1e-4):    if bottleneck:        out = bottleneck_layer(x, nb_channels, kernel_size=kernel_size,                               stride=2, l2_reg=l2_reg)        # The output channels is 4x bigger on this case        nb_channels = nb_channels * 4    else:        out = two_conv_layer(x, nb_channels, kernel_size=kernel_size,                             stride=2, l2_reg=l2_reg)    # Projection on the shortcut    proj = Convolution2D(nb_channels, 1, 1, subsample=(2, 2),                         border_mode='valid', init='he_normal',                         W_regularizer=l2(l2_reg), bias=False)(x)    # proj = AveragePooling2D((1, 1), (2, 2))(x)    out = merge([proj, out], mode='sum')    return out 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def identity_block(x, nb_filter, kernel_size=3):    k1, k2, k3 = nb_filter    shortcut = x     out = Conv2D(k1, kernel_size=(1,1), strides=(1,1),padding="valid",activation="relu")(x)    out = BatchNormalization(axis=3)(out)    out = Conv2D(k2, kernel_size=(3,3), strides=(1,1), padding='same',activation="relu")(out)    out = BatchNormalization(axis=3)(out)    out = Conv2D(k3, kernel_size=(1,1), strides=(1,1),padding="valid")(out)    out = BatchNormalization(axis=3)(out)    # out = merge([out, shortcut], mode='sum')    out= layers.add([out,shortcut])      out = Activation('relu')(out)    return out 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def conv_block(x, nb_filter, kernel_size=3):    k1, k2, k3 = nb_filter    shortcut = x         out = Conv2D(k1, kernel_size=(1,1), strides=(2,2), padding="valid",activation="relu")(x)    out = BatchNormalization(axis=3)(out)    out = out = Conv2D(k2, kernel_size=(kernel_size,kernel_size), strides=(1,1), padding="same",activation="relu")(out)    out = BatchNormalization()(out)    out = Conv2D(k3, kernel_size=(1,1), strides=(1,1), padding="valid")(out)    out = BatchNormalization(axis=3)(out)    shortcut = Conv2D(k3, kernel_size=(1,1), strides=(2,2), padding="valid")(shortcut)    shortcut = BatchNormalization(axis=3)(shortcut)    # out = merge([out, shortcut], mode='sum')    out = layers.add([out, shortcut])    out = Activation('relu')(out)    return out 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def _shortcut(input, residual):    # Expand channels of shortcut to match residual.    # Stride appropriately to match residual (width, height)    # Should be int if network architecture is correctly configured.    stride_width = input._keras_shape[2] / residual._keras_shape[2]    stride_height = input._keras_shape[3] / residual._keras_shape[3]    equal_channels = residual._keras_shape[1] == input._keras_shape[1]    shortcut = input    # 1 X 1 conv if shape is different. Else identity.    if stride_width > 1 or stride_height > 1 or not equal_channels:        shortcut = Convolution2D(nb_filter=residual._keras_shape[1], nb_row=1, nb_col=1,                                 subsample=(stride_width, stride_height),                                 init="he_normal", border_mode="valid")(input)    return merge([shortcut, residual], mode="sum")# Builds a residual block with repeating bottleneck blocks. 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def _up_block(block,mrge, nb_filters):    up = merge([Convolution2D(2*nb_filters, 2, 2, border_mode='same')(UpSampling2D(size=(2, 2))(block)), mrge], mode='concat', concat_axis=1)    # conv = Convolution2D(4*nb_filters, 1, 1, activation='relu', border_mode='same')(up)    conv = Convolution2D(nb_filters, 3, 3, activation='relu', border_mode='same')(up)    conv = Convolution2D(nb_filters, 3, 3, activation='relu', border_mode='same')(conv)    # conv = Convolution2D(4*nb_filters, 1, 1, activation='relu', border_mode='same')(conv)    # conv = Convolution2D(nb_filters, 3, 3, activation='relu', border_mode='same')(conv)    # conv = Convolution2D(nb_filters, 1, 1, activation='relu', border_mode='same')(conv)        # conv = Convolution2D(4*nb_filters, 1, 1, activation='relu', border_mode='same')(conv)    # conv = Convolution2D(nb_filters, 3, 3, activation='relu', border_mode='same')(conv)    # conv = Convolution2D(nb_filters, 1, 1, activation='relu', border_mode='same')(conv)    return conv# http://arxiv.org/pdf/1512.03385v1.pdf# 50 Layer resnet 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def build_model(n_classes):    if K.image_dim_ordering() == 'th':        input_shape = (1, N_MEL_BANDS, SEGMENT_DUR)        channel_axis = 1    else:        input_shape = (N_MEL_BANDS, SEGMENT_DUR, 1)        channel_axis = 3    melgram_input = Input(shape=input_shape)    m_sizes = [50, 70]    n_sizes = [1, 3, 5]    n_filters = [128, 64, 32]    maxpool_const = 4    layers = list()    for m_i in m_sizes:        for i, n_i in enumerate(n_sizes):            x = Convolution2D(n_filters[i], m_i, n_i,                              border_mode='same',                              init='he_normal',                              W_regularizer=l2(1e-5),                              name=str(n_i)+'_'+str(m_i)+'_'+'conv')(melgram_input)            x = BatchNormalization(axis=channel_axis, mode=0, name=str(n_i)+'_'+str(m_i)+'_'+'bn')(x)            x = ELU()(x)            x = MaxPooling2D(pool_size=(N_MEL_BANDS, SEGMENT_DUR/maxpool_const), name=str(n_i)+'_'+str(m_i)+'_'+'pool')(x)            x = Flatten(name=str(n_i)+'_'+str(m_i)+'_'+'flatten')(x)            layers.append(x)    x = merge(layers, mode='concat', concat_axis=channel_axis)    x = Dropout(0.5)(x)    x = Dense(n_classes, init='he_normal', W_regularizer=l2(1e-5), activation='softmax', name='prediction')(x)    model = Model(melgram_input, x)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def build_model(self):        input = Input(shape=self.state_size)        shared = Conv2D(32, (8, 8), strides=(4, 4), activation='relu')(input)        shared = Conv2D(64, (4, 4), strides=(2, 2), activation='relu')(shared)        shared = Conv2D(64, (3, 3), strides=(1, 1), activation='relu')(shared)        flatten = Flatten()(shared)        # network separate state value and advantages        advantage_fc = Dense(512, activation='relu')(flatten)        advantage = Dense(self.action_size)(advantage_fc)        advantage = Lambda(lambda a: a[:, :] - K.mean(a[:, :], keepdims=True),                           output_shape=(self.action_size,))(advantage)        value_fc = Dense(512, activation='relu')(flatten)        value =  Dense(1)(value_fc)        value = Lambda(lambda s: K.expand_dims(s[:, 0], -1),                       output_shape=(self.action_size,))(value)        # network merged and make Q Value        q_value = merge([value, advantage], mode='sum')        model = Model(inputs=input, outputs=q_value)        model.summary()        return model    # after some time interval update the target model to be same with model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def MatchScore(l, r, mode="euclidean"):    if mode == "euclidean":        return merge(            [l, r],            mode=compute_euclidean_match_score,            output_shape=lambda shapes: (None, shapes[0][1], shapes[1][1])        ) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def identity_block(input_tensor, kernel_size, filters, stage, block):    '''The identity_block is the block that has no conv layer at shortcut    # Arguments        input_tensor: input tensor        kernel_size: defualt 3, the kernel size of middle conv layer at main path        filters: list of integers, the nb_filters of 3 conv layer at main path        stage: integer, current stage label, used for generating layer names        block: 'a','b'..., current block label, used for generating layer names    '''    nb_filter1, nb_filter2, nb_filter3 = filters    if K.image_dim_ordering() == 'tf':        bn_axis = 3    else:        bn_axis = 1    conv_name_base = 'res' + str(stage) + block + '_branch'    bn_name_base = 'bn' + str(stage) + block + '_branch'    x = Convolution2D(nb_filter1, 1, 1, name=conv_name_base + '2a')(input_tensor)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)    x = Activation('relu')(x)    x = Convolution2D(nb_filter2, kernel_size, kernel_size,                      border_mode='same', name=conv_name_base + '2b')(x)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)    x = Activation('relu')(x)    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)    x = merge([x, input_tensor], mode='sum')    x = Activation('relu')(x)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):    '''conv_block is the block that has a conv layer at shortcut    # Arguments        input_tensor: input tensor        kernel_size: defualt 3, the kernel size of middle conv layer at main path        filters: list of integers, the nb_filters of 3 conv layer at main path        stage: integer, current stage label, used for generating layer names        block: 'a','b'..., current block label, used for generating layer names    Note that from stage 3, the first conv layer at main path is with subsample=(2,2)    And the shortcut should have subsample=(2,2) as well    '''    nb_filter1, nb_filter2, nb_filter3 = filters    if K.image_dim_ordering() == 'tf':        bn_axis = 3    else:        bn_axis = 1    conv_name_base = 'res' + str(stage) + block + '_branch'    bn_name_base = 'bn' + str(stage) + block + '_branch'    x = Convolution2D(nb_filter1, 1, 1, subsample=strides,                      name=conv_name_base + '2a')(input_tensor)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)    x = Activation('relu')(x)    x = Convolution2D(nb_filter2, kernel_size, kernel_size, border_mode='same',                      name=conv_name_base + '2b')(x)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)    x = Activation('relu')(x)    x = Convolution2D(nb_filter3, 1, 1, name=conv_name_base + '2c')(x)    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)    shortcut = Convolution2D(nb_filter3, 1, 1, subsample=strides,                             name=conv_name_base + '1')(input_tensor)    shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)    x = merge([x, shortcut], mode='sum')    x = Activation('relu')(x)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def cunn_keras(img_rows=FLAGS.img_rows, img_cols=FLAGS.img_cols, channels=FLAGS.nb_channels, nb_classes=FLAGS.nb_classes):    '''    Defines the VGG 16 model using the Keras Sequential model    :param img_rows: number of row in the image    :param img_cols: number of columns in the image    :param channels: number of color channels (e.g., 1 for MNIST)    :param nb_classes: the number of output classes    :return: a Keras model. Call with model(<input_tensor>)    '''    input = Input(shape=(img_rows, img_cols, channels))    conv1 = Convolution2D(32,5,5, border_mode='same', subsample=(1,1), activation='relu')(input)    pool1 = MaxPooling2D((2,2), strides=(2,2))(conv1)    conv2 = Convolution2D(64,5,5, border_mode='same', subsample=(1,1), activation='relu')(pool1)    pool2 = MaxPooling2D((2,2), strides=(2,2))(conv2)    conv3 = Convolution2D(128,5,5, border_mode='same', subsample=(1,1), activation='relu')(pool2)    pool3 = MaxPooling2D((2,2), strides=(2,2))(conv3)    flat1 = Flatten()(pool1)    flat2 = Flatten()(pool2)    flat3 = Flatten()(pool3)    flat_all = merge([flat1, flat2, flat3], mode='concat', concat_axis=1) #If this gives an error, update the keras tensorflow backend. It is likely that is making the call tf.concat(axis, [to_dense(x) for x in tensors]) in of tf.concat([to_dense(x) for x in tensors], axis)    fc = Dense(1024)(flat_all)    drop = Dropout(0.5)(fc)    fc2 = Dense(nb_classes)(drop)    output = Activation('softmax',name='prob')(fc2)    model = Model(input=input, output=output)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def get_model(num_users, num_items, layers = [20,10], reg_layers=[0,0]):    assert len(layers) == len(reg_layers)    num_layer = len(layers) #Number of layers in the MLP    # Input variables    user_input = Input(shape=(1,), dtype='int32', name = 'user_input')    item_input = Input(shape=(1,), dtype='int32', name = 'item_input')    MLP_Embedding_User = Embedding(input_dim = num_users, output_dim = layers[0]/2, name = 'user_embedding',                                  init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)    MLP_Embedding_Item = Embedding(input_dim = num_items, output_dim = layers[0]/2, name = 'item_embedding',                                  init = init_normal, W_regularizer = l2(reg_layers[0]), input_length=1)           # Crucial to flatten an embedding vector!    user_latent = Flatten()(MLP_Embedding_User(user_input))    item_latent = Flatten()(MLP_Embedding_Item(item_input))        # The 0-th layer is the concatenation of embedding layers    vector = merge([user_latent, item_latent], mode = 'concat')        # MLP layers    for idx in xrange(1, num_layer):        layer = Dense(layers[idx], W_regularizer= l2(reg_layers[idx]), activation='relu', name = 'layer%d' %idx)        vector = layer(vector)            # Final prediction layer    prediction = Dense(1, activation='sigmoid', init='lecun_uniform', name = 'prediction')(vector)        model = Model(input=[user_input, item_input],                   output=prediction)        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def Residual(feat_maps_in, feat_maps_out, prev_layer):    '''    A customizable residual unit with convolutional and shortcut blocks    Args:      feat_maps_in: number of channels/filters coming in, from input or previous layer      feat_maps_out: how many output channels/filters this block will produce      prev_layer: the previous layer    '''    skip = skip_block(feat_maps_in, feat_maps_out, prev_layer)    conv = conv_block(feat_maps_out, prev_layer)    print('Residual block mapping '+str(feat_maps_in)+' channels to '+str(feat_maps_out)+' channels built')    return merge([skip, conv], mode='sum') # the residual connection 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def build_MLP_model(self):        NUM_WINDOW_FEATURES = 2        left_token_input = Input(name='left_token_input', shape=(NUM_WINDOW_FEATURES,))        left_token_embedding = Embedding(output_dim=self.preprocessor.embedding_dims, input_dim=self.preprocessor.max_features,                                        input_length=NUM_WINDOW_FEATURES)(left_token_input)        left_token_embedding = Flatten(name="left_token_embedding")(left_token_embedding)        n_PoS_tags = len(self.tag_names)        left_PoS_input = Input(name='left_PoS_input', shape=(n_PoS_tags,))        #target_token_input = Input(name='target_token_input', shape=(1,))        right_token_input = Input(name='right_token_input', shape=(NUM_WINDOW_FEATURES,))        right_token_embedding = Embedding(output_dim=self.preprocessor.embedding_dims, input_dim=self.preprocessor.max_features,                                          input_length=NUM_WINDOW_FEATURES)(right_token_input)        right_PoS_input = Input(name='right_PoS_input', shape=(n_PoS_tags,))        right_token_embedding = Flatten(name="right_token_embedding")(right_token_embedding)        other_features_input = Input(name='other_feature_inputs', shape=(4,))        x = merge([left_token_embedding, #target_token_input,                    right_token_embedding,                    left_PoS_input, right_PoS_input, other_features_input],                    mode='concat', concat_axis=1)        x = Dense(128, name="hidden1", activation='relu')(x)        x = Dropout(.2)(x)        x = Dense(64, name="hidden2", activation='relu')(x)        output = Dense(1, name="prediction", activation='sigmoid')(x)        self.model = Model([left_token_input, left_PoS_input, #target_token_input,                            right_token_input, right_PoS_input, other_features_input],                           output=[output])        self.model.compile(optimizer="adam", loss="binary_crossentropy") 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [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 merge [as 别名]def make_parallel(model, gpu_count):    def get_slice(data, idx, parts):        shape = tf.shape(data)        size = tf.concat(0, [ shape[:1] // parts, shape[1:] ])        stride = tf.concat(0, [ shape[:1] // parts, shape[1:]*0 ])        start = stride * idx        return tf.slice(data, start, size)    outputs_all = []    for i in range(len(model.outputs)):        outputs_all.append([])    #Place a copy of the model on each GPU, each getting a slice of the batch    for i in range(gpu_count):        with tf.device('/gpu:%d' % i):            with tf.name_scope('tower_%d' % i) as scope:                inputs = []                #Slice each input into a piece for processing on this GPU                for x in model.inputs:                    input_shape = tuple(x.get_shape().as_list())[1:]                    slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx':i,'parts':gpu_count})(x)                    inputs.append(slice_n)                                outputs = model(inputs)                                if not isinstance(outputs, list):                    outputs = [outputs]                                #Save all the outputs for merging back together later                for l in range(len(outputs)):                    outputs_all[l].append(outputs[l])    # merge outputs on CPU    with tf.device('/cpu:0'):        merged = []        for outputs in outputs_all:            merged.append(merge(outputs, mode='concat', concat_axis=0))                	return Model(input=model.inputs, output=merged) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def dense_block(x, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1E-4):    ''' Build a dense_block where the output of each conv_block is fed to subsequent ones    Args:        x: keras tensor        nb_layers: the number of layers of conv_block to append to the model.        nb_filter: number of filters        growth_rate: growth rate        dropout_rate: dropout rate        weight_decay: weight decay factor    Returns: keras tensor with nb_layers of conv_block appended    '''    concat_axis = 1 if K.image_dim_ordering() == "th" else -1    feature_list = [x]    for i in range(nb_layers):        x = conv_block(x, growth_rate, dropout_rate, weight_decay)        feature_list.append(x)        x = merge(feature_list, mode='concat', concat_axis=concat_axis)        nb_filter += growth_rate    return x, nb_filter 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def dueling_dqn(input_shape, action_size, learning_rate):        state_input = Input(shape=(input_shape))        x = Convolution2D(32, 8, 8, subsample=(4, 4), activation='relu')(state_input)        x = Convolution2D(64, 4, 4, subsample=(2, 2), activation='relu')(x)        x = Convolution2D(64, 3, 3, activation='relu')(x)        x = Flatten()(x)        # state value tower - V        state_value = Dense(256, activation='relu')(x)        state_value = Dense(1, init='uniform')(state_value)        state_value = Lambda(lambda s: K.expand_dims(s[:, 0], dim=-1), output_shape=(action_size,))(state_value)        # action advantage tower - A        action_advantage = Dense(256, activation='relu')(x)        action_advantage = Dense(action_size)(action_advantage)        action_advantage = Lambda(lambda a: a[:, :] - K.mean(a[:, :], keepdims=True), output_shape=(action_size,))(action_advantage)        # merge to state-action value function Q        state_action_value = merge([state_value, action_advantage], mode='sum')        model = Model(input=state_input, output=state_action_value)        #model.compile(rmsprop(lr=learning_rate), "mse")        adam = Adam(lr=learning_rate)        model.compile(loss='mse',optimizer=adam)        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def concatenate_layers(inputs, concat_axis, mode='concat'):    if KERAS_2:        assert mode == 'concat', "Only concatenation is supported in this wrapper"        return Concatenate(axis=concat_axis)(inputs)    else:        return merge(inputs=inputs, concat_axis=concat_axis, mode=mode) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import merge [as 别名]def residual_drop(x, input_shape, output_shape, strides=(1, 1)):    global add_tables    nb_filter = output_shape[0]    conv = Convolution2D(nb_filter, 3, 3, subsample=strides,                         border_mode="same", W_regularizer=l2(weight_decay))(x)    conv = BatchNormalization(axis=1)(conv)    conv = Activation("relu")(conv)    conv = Convolution2D(nb_filter, 3, 3,                         border_mode="same", W_regularizer=l2(weight_decay))(conv)    conv = BatchNormalization(axis=1)(conv)    if strides[0] >= 2:        x = AveragePooling2D(strides)(x)    if (output_shape[0] - input_shape[0]) > 0:        pad_shape = (1,                     output_shape[0] - input_shape[0],                     output_shape[1],                     output_shape[2])        padding = K.zeros(pad_shape)        padding = K.repeat_elements(padding, K.shape(x)[0], axis=0)        x = Lambda(lambda y: K.concatenate([y, padding], axis=1),                   output_shape=output_shape)(x)    _death_rate = K.variable(death_rate)    scale = K.ones_like(conv) - _death_rate    conv = Lambda(lambda c: K.in_test_phase(scale * c, c),                  output_shape=output_shape)(conv)    out = merge([conv, x], mode="sum")    out = Activation("relu")(out)    gate = K.variable(1, dtype="uint8")    add_tables += [{"death_rate": _death_rate, "gate": gate}]    return Lambda(lambda tensors: K.switch(gate, tensors[0], tensors[1]),                  output_shape=output_shape)([out, x])