自学教程：Python layers.Dropout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 Dropout [as 别名]def _makenet(x, num_layers, dropout, random_seed):    from keras.layers import Dense, Dropout    dropout_seeder = random.Random(random_seed)    for i in range(num_layers - 1):        # add intermediate layers        if dropout:            x = Dropout(dropout, seed=dropout_seeder.randint(0, 10000))(x)        x = Dense(1024, activation="relu", name='dense_layer_{}'.format(i))(x)    if dropout:        # add the final dropout layer        x = Dropout(dropout, seed=dropout_seeder.randint(0, 10000))(x)    return x 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def __init__(self, model_path=None):        if model_path is not None:            self.model = self.load_model(model_path)        else:            # VGG16 last conv features            inputs = Input(shape=(7, 7, 512))            x = Convolution2D(128, 1, 1)(inputs)            x = Flatten()(x)            # Cls head            h_cls = Dense(256, activation='relu', W_regularizer=l2(l=0.01))(x)            h_cls = Dropout(p=0.5)(h_cls)            cls_head = Dense(20, activation='softmax', name='cls')(h_cls)            # Reg head            h_reg = Dense(256, activation='relu', W_regularizer=l2(l=0.01))(x)            h_reg = Dropout(p=0.5)(h_reg)            reg_head = Dense(4, activation='linear', name='reg')(h_reg)            # Joint model            self.model = Model(input=inputs, output=[cls_head, reg_head]) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def build_generator(self):        X = Input(shape=(self.img_dim,))        model = Sequential()        model.add(Dense(256, input_dim=self.img_dim))        model.add(LeakyReLU(alpha=0.2))        model.add(BatchNormalization(momentum=0.8))        model.add(Dropout(0.4))        model.add(Dense(512))        model.add(LeakyReLU(alpha=0.2))        model.add(BatchNormalization(momentum=0.8))        model.add(Dropout(0.4))        model.add(Dense(1024))        model.add(LeakyReLU(alpha=0.2))        model.add(BatchNormalization(momentum=0.8))        model.add(Dropout(0.4))        model.add(Dense(self.img_dim, activation='tanh'))        X_translated = model(X)        return Model(X, X_translated) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [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 Dropout [as 别名]def modelA():    model = Sequential()    model.add(Conv2D(64, (5, 5),                            padding='valid'))    model.add(Activation('relu'))    model.add(Conv2D(64, (5, 5)))    model.add(Activation('relu'))    model.add(Dropout(0.25))    model.add(Flatten())    model.add(Dense(128))    model.add(Activation('relu'))    model.add(Dropout(0.5))    model.add(Dense(FLAGS.NUM_CLASSES))    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def modelB():    model = Sequential()    model.add(Dropout(0.2, input_shape=(FLAGS.IMAGE_ROWS,                                        FLAGS.IMAGE_COLS,                                        FLAGS.NUM_CHANNELS)))    model.add(Convolution2D(64, 8, 8,                            subsample=(2, 2),                            border_mode='same'))    model.add(Activation('relu'))    model.add(Convolution2D(128, 6, 6,                            subsample=(2, 2),                            border_mode='valid'))    model.add(Activation('relu'))    model.add(Convolution2D(128, 5, 5,                            subsample=(1, 1)))    model.add(Activation('relu'))    model.add(Dropout(0.5))    model.add(Flatten())    model.add(Dense(FLAGS.NUM_CLASSES))    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def modelC():    model = Sequential()    model.add(Convolution2D(128, 3, 3,                            border_mode='valid',                            input_shape=(FLAGS.IMAGE_ROWS,                                         FLAGS.IMAGE_COLS,                                         FLAGS.NUM_CHANNELS)))    model.add(Activation('relu'))    model.add(Convolution2D(64, 3, 3))    model.add(Activation('relu'))    model.add(Dropout(0.25))    model.add(Flatten())    model.add(Dense(128))    model.add(Activation('relu'))    model.add(Dropout(0.5))    model.add(Dense(FLAGS.NUM_CLASSES))    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def modelD():    model = Sequential()    model.add(Flatten(input_shape=(FLAGS.IMAGE_ROWS,                                   FLAGS.IMAGE_COLS,                                   FLAGS.NUM_CHANNELS)))    model.add(Dense(300, init='he_normal', activation='relu'))    model.add(Dropout(0.5))    model.add(Dense(300, init='he_normal', activation='relu'))    model.add(Dropout(0.5))    model.add(Dense(300, init='he_normal', activation='relu'))    model.add(Dropout(0.5))    model.add(Dense(300, init='he_normal', activation='relu'))    model.add(Dropout(0.5))    model.add(Dense(FLAGS.NUM_CLASSES))    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def ann_model(input_shape):    inp = Input(shape=input_shape, name='mfcc_in')    model = inp    model = Conv1D(filters=12, kernel_size=(3), activation='relu')(model)    model = Conv1D(filters=12, kernel_size=(3), activation='relu')(model)    model = Flatten()(model)    model = Dense(56)(model)    model = Activation('relu')(model)    model = BatchNormalization()(model)    model = Dropout(0.2)(model)    model = Dense(28)(model)    model = Activation('relu')(model)    model = BatchNormalization()(model)    model = Dense(1)(model)    model = Activation('sigmoid')(model)    model = Model(inp, model)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def buildModel_DNN(Shape, nClasses, nLayers=3,Number_Node=100, dropout=0.5):    '''    buildModel_DNN(nFeatures, nClasses, nLayers=3,Numberof_NOde=100, dropout=0.5)    Build Deep neural networks (Multi-layer perceptron) Model for text classification    Shape is input feature space    nClasses is number of classes    nLayers is number of hidden Layer    Number_Node is number of unit in each hidden layer    dropout is dropout value for solving overfitting problem    '''    model = Sequential()    model.add(Dense(Number_Node, input_dim=Shape))    model.add(Dropout(dropout))    for i in range(0,nLayers):        model.add(Dense(Number_Node, activation='relu'))        model.add(Dropout(dropout))    model.add(Dense(nClasses, activation='softmax'))    model.compile(loss='sparse_categorical_crossentropy',                  optimizer='RMSprop',                  metrics=['accuracy'])    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def weather_fnn(layers, lr,            decay, loss, seq_len,             input_features, output_features):        ori_inputs = Input(shape=(seq_len, input_features), name='input_layer')    #print(seq_len*input_features)    conv_ = Conv1D(11, kernel_size=13, strides=1,                         data_format='channels_last',                         padding='valid', activation='linear')(ori_inputs)    conv_ = BatchNormalization(name='BN_conv')(conv_)    conv_ = Activation('relu')(conv_)    conv_ = Conv1D(5, kernel_size=7, strides=1,                         data_format='channels_last',                         padding='valid', activation='linear')(conv_)    conv_ = BatchNormalization(name='BN_conv2')(conv_)    conv_ = Activation('relu')(conv_)    inputs = Reshape((-1,))(conv_)    for i, hidden_nums in enumerate(layers):        if i==0:            hn = Dense(hidden_nums, activation='linear')(inputs)            hn = BatchNormalization(name='BN_{}'.format(i))(hn)            hn = Activation('relu')(hn)        else:            hn = Dense(hidden_nums, activation='linear')(hn)            hn = BatchNormalization(name='BN_{}'.format(i))(hn)            hn = Activation('relu')(hn)            #hn = Dropout(0.1)(hn)    #print(seq_len, output_features)    #print(hn)    outputs = Dense(seq_len*output_features, activation='sigmoid', name='output_layer')(hn) # 37*3    outputs = Reshape((seq_len, output_features))(outputs)    weather_fnn = Model(ori_inputs, outputs=[outputs])    return weather_fnn 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def _get_model(X, cat_cols, num_cols, n_uniq, n_emb, output_activation):        inputs = []        num_inputs = []        embeddings = []        for i, col in enumerate(cat_cols):            if not n_uniq[i]:                n_uniq[i] = X[col].nunique()            if not n_emb[i]:                n_emb[i] = max(MIN_EMBEDDING, 2 * int(np.log2(n_uniq[i])))            _input = Input(shape=(1,), name=col)            _embed = Embedding(input_dim=n_uniq[i], output_dim=n_emb[i], name=col + EMBEDDING_SUFFIX)(_input)            _embed = Dropout(.2)(_embed)            _embed = Reshape((n_emb[i],))(_embed)            inputs.append(_input)            embeddings.append(_embed)        if num_cols:            num_inputs = Input(shape=(len(num_cols),), name='num_inputs')            merged_input = Concatenate(axis=1)(embeddings + [num_inputs])            inputs = inputs + [num_inputs]        else:            merged_input = Concatenate(axis=1)(embeddings)        x = BatchNormalization()(merged_input)        x = Dense(128, activation='relu')(x)        x = Dropout(.5)(x)        x = BatchNormalization()(x)        x = Dense(64, activation='relu')(x)        x = Dropout(.5)(x)        x = BatchNormalization()(x)        output = Dense(1, activation=output_activation)(x)        model = Model(inputs=inputs, outputs=output)        return model, n_emb, n_uniq 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def build_discriminator(self):        model = Sequential()        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))        model.add(ZeroPadding2D(padding=((0,1),(0,1))))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(BatchNormalization(momentum=0.8))        model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(BatchNormalization(momentum=0.8))        model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Flatten())        model.summary()        img = Input(shape=self.img_shape)        features = model(img)        valid = Dense(1, activation="sigmoid")(features)        label = Dense(self.num_classes+1, activation="softmax")(features)        return Model(img, [valid, label]) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def build_generator(self):        model = Sequential()        # Encoder        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(BatchNormalization(momentum=0.8))        model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(BatchNormalization(momentum=0.8))        model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(BatchNormalization(momentum=0.8))        model.add(Conv2D(512, kernel_size=1, strides=2, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.5))        # Decoder        model.add(UpSampling2D())        model.add(Conv2D(128, kernel_size=3, padding="same"))        model.add(Activation('relu'))        model.add(BatchNormalization(momentum=0.8))        model.add(UpSampling2D())        model.add(Conv2D(64, kernel_size=3, padding="same"))        model.add(Activation('relu'))        model.add(BatchNormalization(momentum=0.8))        model.add(Conv2D(self.channels, kernel_size=3, padding="same"))        model.add(Activation('tanh'))        model.summary()        masked_img = Input(shape=self.img_shape)        gen_missing = model(masked_img)        return Model(masked_img, gen_missing) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def build_generator(self):        """U-Net Generator"""        def conv2d(layer_input, filters, f_size=4, bn=True):            """Layers used during downsampling"""            d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)            d = LeakyReLU(alpha=0.2)(d)            if bn:                d = BatchNormalization(momentum=0.8)(d)            return d        def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0):            """Layers used during upsampling"""            u = UpSampling2D(size=2)(layer_input)            u = Conv2D(filters, kernel_size=f_size, strides=1, padding='same', activation='relu')(u)            if dropout_rate:                u = Dropout(dropout_rate)(u)            u = BatchNormalization(momentum=0.8)(u)            u = Concatenate()([u, skip_input])            return u        img = Input(shape=self.img_shape)        # Downsampling        d1 = conv2d(img, self.gf, bn=False)        d2 = conv2d(d1, self.gf*2)        d3 = conv2d(d2, self.gf*4)        d4 = conv2d(d3, self.gf*8)        # Upsampling        u1 = deconv2d(d4, d3, self.gf*4)        u2 = deconv2d(u1, d2, self.gf*2)        u3 = deconv2d(u2, d1, self.gf)        u4 = UpSampling2D(size=2)(u3)        output_img = Conv2D(self.channels, kernel_size=4, strides=1, padding='same', activation='tanh')(u4)        return Model(img, output_img) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def build_disk_and_q_net(self):        img = Input(shape=self.img_shape)        # Shared layers between discriminator and recognition network        model = Sequential()        model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))        model.add(ZeroPadding2D(padding=((0,1),(0,1))))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(BatchNormalization(momentum=0.8))        model.add(Conv2D(256, kernel_size=3, strides=2, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(BatchNormalization(momentum=0.8))        model.add(Conv2D(512, kernel_size=3, strides=2, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(BatchNormalization(momentum=0.8))        model.add(Flatten())        img_embedding = model(img)        # Discriminator        validity = Dense(1, activation='sigmoid')(img_embedding)        # Recognition        q_net = Dense(128, activation='relu')(img_embedding)        label = Dense(self.num_classes, activation='softmax')(q_net)        # Return discriminator and recognition network        return Model(img, validity), Model(img, label) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def build_critic(self):        model = Sequential()        model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))        model.add(ZeroPadding2D(padding=((0,1),(0,1))))        model.add(BatchNormalization(momentum=0.8))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))        model.add(BatchNormalization(momentum=0.8))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))        model.add(BatchNormalization(momentum=0.8))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Flatten())        model.add(Dense(1))        model.summary()        img = Input(shape=self.img_shape)        validity = model(img)        return Model(img, validity) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def build_generator(self):        """U-Net Generator"""        def conv2d(layer_input, filters, f_size=4):            """Layers used during downsampling"""            d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)            d = LeakyReLU(alpha=0.2)(d)            d = InstanceNormalization()(d)            return d        def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0):            """Layers used during upsampling"""            u = UpSampling2D(size=2)(layer_input)            u = Conv2D(filters, kernel_size=f_size, strides=1, padding='same', activation='relu')(u)            if dropout_rate:                u = Dropout(dropout_rate)(u)            u = InstanceNormalization()(u)            u = Concatenate()([u, skip_input])            return u        # Image input        d0 = Input(shape=self.img_shape)        # Downsampling        d1 = conv2d(d0, self.gf)        d2 = conv2d(d1, self.gf*2)        d3 = conv2d(d2, self.gf*4)        d4 = conv2d(d3, self.gf*8)        # Upsampling        u1 = deconv2d(d4, d3, self.gf*4)        u2 = deconv2d(u1, d2, self.gf*2)        u3 = deconv2d(u2, d1, self.gf)        u4 = UpSampling2D(size=2)(u3)        output_img = Conv2D(self.channels, kernel_size=4, strides=1, padding='same', activation='tanh')(u4)        return Model(d0, output_img) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def build_discriminator(self):        model = Sequential()        model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))        model.add(ZeroPadding2D(padding=((0,1),(0,1))))        model.add(BatchNormalization(momentum=0.8))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))        model.add(BatchNormalization(momentum=0.8))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))        model.add(BatchNormalization(momentum=0.8))        model.add(LeakyReLU(alpha=0.2))        model.add(Dropout(0.25))        model.add(Flatten())        model.add(Dense(1, activation='sigmoid'))        model.summary()        img = Input(shape=self.img_shape)        validity = model(img)        return Model(img, validity) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def get_model_3(params):    # metadata    inputs2 = Input(shape=(params["n_metafeatures"],))    x2 = Dropout(params["dropout_factor"])(inputs2)    if params["n_dense"] > 0:        dense2 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu')        x2 = dense2(x2)        logging.debug("Output CNN: %s" % str(dense2.output_shape))        x2 = Dropout(params["dropout_factor"])(x2)    if params["n_dense_2"] > 0:        dense3 = Dense(output_dim=params["n_dense_2"], init="uniform", activation='relu')        x2 = dense3(x2)        logging.debug("Output CNN: %s" % str(dense3.output_shape))        x2 = Dropout(params["dropout_factor"])(x2)    dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation'])    xout = dense4(x2)    logging.debug("Output CNN: %s" % str(dense4.output_shape))    if params['final_activation'] == 'linear':        reg = Lambda(lambda x :K.l2_normalize(x, axis=1))        xout = reg(xout)    model = Model(input=inputs2, output=xout)    return model# Metadata 2 inputs, post-merge with dense layers 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def get_model_32(params):    # metadata    inputs = Input(shape=(params["n_metafeatures"],))    reg = Lambda(lambda x :K.l2_normalize(x, axis=1))    x1 = reg(inputs)    inputs2 = Input(shape=(params["n_metafeatures2"],))    reg2 = Lambda(lambda x :K.l2_normalize(x, axis=1))    x2 = reg2(inputs2)    # merge    x = merge([x1, x2], mode='concat', concat_axis=1)    x = Dropout(params["dropout_factor"])(x)    if params['n_dense'] > 0:        dense2 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu')        x = dense2(x)        logging.debug("Output CNN: %s" % str(dense2.output_shape))    dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation'])    xout = dense4(x)    logging.debug("Output CNN: %s" % str(dense4.output_shape))    if params['final_activation'] == 'linear':        reg = Lambda(lambda x :K.l2_normalize(x, axis=1))        xout = reg(xout)    model = Model(input=[inputs,inputs2], output=xout)    return model# Metadata 3 inputs, pre-merge and l2 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def get_model_33(params):    # metadata    inputs = Input(shape=(params["n_metafeatures"],))    reg = Lambda(lambda x :K.l2_normalize(x, axis=1))    x1 = reg(inputs)    inputs2 = Input(shape=(params["n_metafeatures2"],))    reg2 = Lambda(lambda x :K.l2_normalize(x, axis=1))    x2 = reg2(inputs2)    inputs3 = Input(shape=(params["n_metafeatures3"],))    reg3 = Lambda(lambda x :K.l2_normalize(x, axis=1))    x3 = reg3(inputs3)    # merge    x = merge([x1, x2, x3], mode='concat', concat_axis=1)    x = Dropout(params["dropout_factor"])(x)    if params['n_dense'] > 0:        dense2 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu')        x = dense2(x)        logging.debug("Output CNN: %s" % str(dense2.output_shape))    dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation'])    xout = dense4(x)    logging.debug("Output CNN: %s" % str(dense4.output_shape))    if params['final_activation'] == 'linear':        reg = Lambda(lambda x :K.l2_normalize(x, axis=1))        xout = reg(xout)    model = Model(input=[inputs,inputs2,inputs3], output=xout)    return model# Metadata 4 inputs, pre-merge and l2 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def get_model_34(params):    # metadata    inputs = Input(shape=(params["n_metafeatures"],))    reg = Lambda(lambda x :K.l2_normalize(x, axis=1))    x1 = reg(inputs)    inputs2 = Input(shape=(params["n_metafeatures2"],))    reg2 = Lambda(lambda x :K.l2_normalize(x, axis=1))    x2 = reg2(inputs2)    inputs3 = Input(shape=(params["n_metafeatures3"],))    reg3 = Lambda(lambda x :K.l2_normalize(x, axis=1))    x3 = reg3(inputs3)    inputs4 = Input(shape=(params["n_metafeatures4"],))    reg4 = Lambda(lambda x :K.l2_normalize(x, axis=1))    x4 = reg4(inputs4)    # merge    x = merge([x1, x2, x3, x4], mode='concat', concat_axis=1)    x = Dropout(params["dropout_factor"])(x)    if params['n_dense'] > 0:        dense2 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu')        x = dense2(x)        logging.debug("Output CNN: %s" % str(dense2.output_shape))    dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation'])    xout = dense4(x)    logging.debug("Output CNN: %s" % str(dense4.output_shape))    if params['final_activation'] == 'linear':        reg = Lambda(lambda x :K.l2_normalize(x, axis=1))        xout = reg(xout)    model = Model(input=[inputs,inputs2,inputs3,inputs4], output=xout)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def get_model_6(params):    # metadata    inputs2 = Input(shape=(params["n_metafeatures"],))    #x2 = Dropout(params["dropout_factor"])(inputs2)    if params["n_dense"] > 0:        dense21 = Dense(output_dim=params["n_dense"], init="uniform", activation='relu')        x21 = dense21(inputs2)        logging.debug("Output CNN: %s" % str(dense21.output_shape))        dense22 = Dense(output_dim=params["n_dense"], init="uniform", activation='tanh')        x22 = dense22(inputs2)        logging.debug("Output CNN: %s" % str(dense22.output_shape))        dense23 = Dense(output_dim=params["n_dense"], init="uniform", activation='sigmoid')        x23 = dense23(inputs2)        logging.debug("Output CNN: %s" % str(dense23.output_shape))        # merge        x = merge([x21, x22, x23], mode='concat', concat_axis=1)        x2 = Dropout(params["dropout_factor"])(x)    if params["n_dense_2"] > 0:        dense3 = Dense(output_dim=params["n_dense_2"], init="uniform", activation='relu')        x2 = dense3(x2)        logging.debug("Output CNN: %s" % str(dense3.output_shape))        x2 = Dropout(params["dropout_factor"])(x2)    dense4 = Dense(output_dim=params["n_out"], init="uniform", activation=params['final_activation'])    xout = dense4(x2)    logging.debug("Output CNN: %s" % str(dense4.output_shape))    if params['final_activation'] == 'linear':        reg = Lambda(lambda x :K.l2_normalize(x, axis=1))        xout = reg(xout)    model = Model(input=inputs2, output=xout)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def creat_model(input_shape, num_class):    init = initializers.Orthogonal(gain=args.norm)    sequence_input =Input(shape=input_shape)    mask = Masking(mask_value=0.)(sequence_input)    if args.aug:        mask = augmentaion()(mask)    X = Noise(0.075)(mask)    if args.model[0:2]=='VA':        # VA        trans = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)        trans = Dropout(0.5)(trans)        trans = TimeDistributed(Dense(3,kernel_initializer='zeros'))(trans)        rot = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)        rot = Dropout(0.5)(rot)        rot = TimeDistributed(Dense(3,kernel_initializer='zeros'))(rot)        transform = Concatenate()([rot,trans])        X = VA()([mask,transform])    X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)    X = Dropout(0.5)(X)    X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)    X = Dropout(0.5)(X)    X = LSTM(args.nhid,recurrent_activation='sigmoid',return_sequences=True,implementation=2,recurrent_initializer=init)(X)    X = Dropout(0.5)(X)    X = TimeDistributed(Dense(num_class))(X)    X = MeanOverTime()(X)    X = Activation('softmax')(X)    model=Model(sequence_input,X)    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Dropout [as 别名]def load_model():    from keras.models import Model    from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D        tensor_in = Input((60, 200, 3))    out = tensor_in    out = Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(out)    out = Conv2D(filters=32, kernel_size=(3, 3), activation='relu')(out)    out = MaxPooling2D(pool_size=(2, 2))(out)    out = Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')(out)    out = Conv2D(filters=64, kernel_size=(3, 3), activation='relu')(out)    out = MaxPooling2D(pool_size=(2, 2))(out)    out = Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='relu')(out)    out = Conv2D(filters=128, kernel_size=(3, 3), activation='relu')(out)    out = MaxPooling2D(pool_size=(2, 2))(out)    out = Conv2D(filters=256, kernel_size=(3, 3), activation='relu')(out)    out = MaxPooling2D(pool_size=(2, 2))(out)    out = Flatten()(out)    out = Dropout(0.5)(out)    out = [Dense(37, name='digit1', activation='softmax')(out),/        Dense(37, name='digit2', activation='softmax')(out),/        Dense(37, name='digit3', activation='softmax')(out),/        Dense(37, name='digit4', activation='softmax')(out),/        Dense(37, name='digit5', activation='softmax')(out),/        Dense(37, name='digit6', activation='softmax')(out)]        model = Model(inputs=tensor_in, outputs=out)        # Define the optimizer    model.compile(loss='categorical_crossentropy', optimizer='Adamax', metrics=['accuracy'])    if 'Windows' in platform.platform():        model.load_weights('{}//cnn_weight//verificatioin_code.h5'.format(PATH))     else:        model.load_weights('{}/cnn_weight/verificatioin_code.h5'.format(PATH))         return model