Question

from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from tensorflow.keras.layers import Dropout, Flatten, Input, Dense

def create_model():

    def add_conv_block(model, num_filters):

        model.add(Conv2D(num_filters, 3, activation='relu', padding='same'))
        model.add(BatchNormalization())
        model.add(Conv2D(num_filters, 3, activation='relu', padding='valid'))
        model.add(MaxPooling2D(pool_size=2))
        model.add(Dropout(0.2))

        return model

    model = tf.keras.models.Sequential()
    model.add(Input(shape=(32, 32, 3)))

    model = add_conv_block(model, 32)
    model = add_conv_block(model, 64)
    model = add_conv_block(model, 128)

    model.add(Flatten())
    model.add(Dense(3, activation='softmax'))

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

model = create_model()
model.summary()

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Answer 1

我认为问题与TF版本有关...但是我建议您使用此实现。这样，您可以在顺序模型的第一层中指定input_shape并覆盖问题

def create_model():

    def add_conv_block(model, num_filters, input_shape=None):

        if input_shape:
            model.add(Conv2D(num_filters, 3, activation='relu', padding='same', input_shape=input_shape))
        else:
            model.add(Conv2D(num_filters, 3, activation='relu', padding='same'))

        model.add(BatchNormalization())
        model.add(Conv2D(num_filters, 3, activation='relu', padding='valid'))
        model.add(MaxPooling2D(pool_size=2))
        model.add(Dropout(0.2))

        return model

    model = tf.keras.models.Sequential()
    model = add_conv_block(model, 32, input_shape=(32, 32, 3))
    model = add_conv_block(model, 64)
    model = add_conv_block(model, 128)

    model.add(Flatten())
    model.add(Dense(3, activation='softmax'))

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

    return model

model = create_model()
model.summary()

Answer 2

解决方案是使用InputLayer而不是Input。 InputLayer用于Sequential模型。您也可以完全省略InputLayer并在顺序模型的第一层中指定input_shape。

Input旨在与TensorFlow Keras功能API一起使用，而不是与顺序API一起使用。

from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from tensorflow.keras.layers import Dropout, Flatten, InputLayer, Dense

def create_model():

    def add_conv_block(model, num_filters):

        model.add(Conv2D(num_filters, 3, activation='relu', padding='same'))
        model.add(BatchNormalization())
        model.add(Conv2D(num_filters, 3, activation='relu', padding='valid'))
        model.add(MaxPooling2D(pool_size=2))
        model.add(Dropout(0.2))

        return model

    model = tf.keras.models.Sequential()
    model.add(InputLayer((32, 32, 3)))

    model = add_conv_block(model, 32)
    model = add_conv_block(model, 64)
    model = add_conv_block(model, 128)

    model.add(Flatten())
    model.add(Dense(3, activation='softmax'))

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

model = create_model()
model.summary()

Keras创建CNN模型“添加的层必须是Layer类的实例”

2 个答案: