自学教程：Python layers.Subtract方法代码示例

示例1: _build_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Subtract [as 别名]def _build_model(input_shape, hidden_layer_sizes, activation):        """        Build Keras Ranker NN model (Ranknet / LambdaRank NN).        """        # Neural network structure        hidden_layers = []        for i in range(len(hidden_layer_sizes)):            hidden_layers.append(Dense(hidden_layer_sizes[i], activation=activation[i], name=str(activation[i]) + '_layer' + str(i)))        h0 = Dense(1, activation='linear', name='Identity_layer')        input1 = Input(shape=(input_shape,), name='Input_layer1')        input2 = Input(shape=(input_shape,), name='Input_layer2')        x1 = input1        x2 = input2        for i in range(len(hidden_layer_sizes)):            x1 = hidden_layers[i](x1)            x2 = hidden_layers[i](x2)        x1 = h0(x1)        x2 = h0(x2)        # Subtract layer        subtracted = Subtract(name='Subtract_layer')([x1, x2])        # sigmoid        out = Activation('sigmoid', name='Activation_layer')(subtracted)        # build model        model = Model(inputs=[input1, input2], outputs=out)        return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Subtract [as 别名]def DnCNN():        inpt = Input(shape=(None,None,1))    # 1st layer, Conv+relu    x = Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='same')(inpt)    x = Activation('relu')(x)    # 15 layers, Conv+BN+relu    for i in range(15):        x = Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='same')(x)        x = BatchNormalization(axis=-1, epsilon=1e-3)(x)        x = Activation('relu')(x)       # last layer, Conv    x = Conv2D(filters=1, kernel_size=(3,3), strides=(1,1), padding='same')(x)    x = Subtract()([inpt, x])   # input - noise    model = Model(inputs=inpt, outputs=x)        return model 

示例3: test_merge_subtract

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Subtract [as 别名]def test_merge_subtract():    i1 = layers.Input(shape=(4, 5))    i2 = layers.Input(shape=(4, 5))    i3 = layers.Input(shape=(4, 5))    i4 = layers.Input(shape=(3, 5))    o = layers.subtract([i1, i2])    assert o._keras_shape == (None, 4, 5)    model = models.Model([i1, i2], o)    subtract_layer = layers.Subtract()    o2 = subtract_layer([i1, i2])    assert subtract_layer.output_shape == (None, 4, 5)    x1 = np.random.random((2, 4, 5))    x2 = np.random.random((2, 4, 5))    out = model.predict([x1, x2])    assert out.shape == (2, 4, 5)    assert_allclose(out, x1 - x2, atol=1e-4)    assert subtract_layer.compute_mask([i1, i2], [None, None]) is None    assert np.all(K.eval(subtract_layer.compute_mask(        [i1, i2], [K.variable(x1), K.variable(x2)])))    # Test invalid use case    with pytest.raises(ValueError):        subtract_layer.compute_mask([i1, i2], x1)    with pytest.raises(ValueError):        subtract_layer.compute_mask(i1, [None, None])    with pytest.raises(ValueError):        subtract_layer([i1, i2, i3])    with pytest.raises(ValueError):        subtract_layer([i1]) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Subtract [as 别名]def baseline_model():    input_1 = Input(shape=(224, 224, 3))    input_2 = Input(shape=(224, 224, 3))    base_model = VGGFace(model='resnet50', include_top=False)    for x in base_model.layers[:-3]:        x.trainable = True    x1 = base_model(input_1)    x2 = base_model(input_2)    # x1_ = Reshape(target_shape=(7*7, 2048))(x1)    # x2_ = Reshape(target_shape=(7*7, 2048))(x2)    #    # x_dot = Dot(axes=[2, 2], normalize=True)([x1_, x2_])    # x_dot = Flatten()(x_dot)    x1 = Concatenate(axis=-1)([GlobalMaxPool2D()(x1), GlobalAvgPool2D()(x1)])    x2 = Concatenate(axis=-1)([GlobalMaxPool2D()(x2), GlobalAvgPool2D()(x2)])    x3 = Subtract()([x1, x2])    x3 = Multiply()([x3, x3])    x = Multiply()([x1, x2])    x = Concatenate(axis=-1)([x, x3])    x = Dense(100, activation="relu")(x)    x = Dropout(0.01)(x)    out = Dense(1, activation="sigmoid")(x)    model = Model([input_1, input_2], out)    model.compile(loss="binary_crossentropy", metrics=['acc'], optimizer=Adam(0.00001))    model.summary()    return model 

示例5: baseline_model

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Subtract [as 别名]def baseline_model():    input_1 = Input(shape=(224, 224, 3))    input_2 = Input(shape=(224, 224, 3))    base_model = ResNet50(weights='imagenet', include_top=False)    for x in base_model.layers[:-3]:        x.trainable = True    x1 = base_model(input_1)    x2 = base_model(input_2)    # x1_ = Reshape(target_shape=(7*7, 2048))(x1)    # x2_ = Reshape(target_shape=(7*7, 2048))(x2)    #    # x_dot = Dot(axes=[2, 2], normalize=True)([x1_, x2_])    # x_dot = Flatten()(x_dot)    x1 = Concatenate(axis=-1)([GlobalMaxPool2D()(x1), GlobalAvgPool2D()(x1)])    x2 = Concatenate(axis=-1)([GlobalMaxPool2D()(x2), GlobalAvgPool2D()(x2)])    x3 = Subtract()([x1, x2])    x3 = Multiply()([x3, x3])    x = Multiply()([x1, x2])    x = Concatenate(axis=-1)([x, x3])    x = Dense(100, activation="relu")(x)    x = Dropout(0.01)(x)    out = Dense(1, activation="sigmoid")(x)    model = Model([input_1, input_2], out)    model.compile(loss="binary_crossentropy", metrics=['acc'], optimizer=Adam(0.00001))    model.summary()    return model 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import Subtract [as 别名]def get_Discriminator(input_shape_1, input_shape_2, Encoder):    dis_inputs_1 = Input(shape=input_shape_1) # Image    dis_inputs_2 = Input(shape=input_shape_2) # Segmentation    mul_1 = Multiply()([dis_inputs_1, dis_inputs_2]) # Getting segmented part    encoder_output_1 = Encoder(dis_inputs_1)    encoder_output_2 = Encoder(mul_1)    subtract_dis = Subtract()([encoder_output_1, encoder_output_2])    dis_conv_block = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(subtract_dis)    dis_conv_block = Activation('relu')(dis_conv_block)    dis_conv_block = Conv3D(128, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)    dis_conv_block = Activation('relu')(dis_conv_block)    dis_conv_block = MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2))(dis_conv_block)    dis_conv_block = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)    dis_conv_block = Activation('relu')(dis_conv_block)    dis_conv_block = Conv3D(64, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)    dis_conv_block = Activation('relu')(dis_conv_block)    dis_conv_block = Conv3D(32, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)    dis_conv_block = Activation('relu')(dis_conv_block)    dis_conv_block = Conv3D(32, (3, 3, 3), strides=(1, 1, 1), padding='same')(dis_conv_block)    dis_conv_block = Activation('relu')(dis_conv_block)    flat_1 = Flatten()(dis_conv_block)    dis_fc_1 = Dense(256)(flat_1)    dis_fc_1 = Activation('relu')(dis_fc_1)    dis_drp_1 = Dropout(0.5)(dis_fc_1)    dis_fc_2 = Dense(128)(dis_drp_1)    dis_fc_2 = Activation('relu')(dis_fc_2)    dis_drp_2 = Dropout(0.5)(dis_fc_2)    dis_fc_3 = Dense(1)(dis_drp_2)    dis_similarity_output = Activation('sigmoid')(dis_fc_3)    Discriminator = Model(inputs=[dis_inputs_1, dis_inputs_2], outputs=dis_similarity_output)    Discriminator.compile(optimizer=Adadelta(lr=0.01), loss='binary_crossentropy', metrics=['accuracy'])    print('Discriminator Architecture:')    print(Discriminator.summary())    return Discriminator 

注：本文中的keras.layers.Subtract方法
Python layers.AveragePooling1D方法代码示例
Python layers.GaussianNoise方法代码示例