python - Keras 创建 CNN 模型“添加的层必须是类层的实例”

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()

在此处输入图像描述

标签： pythontensorflowkerasconv-neural-network

Answer 1

解决方案是使用InputLayer而不是Input. InputLayer旨在与Sequential模型一起使用。您也可以InputLayer完全省略并input_shape在顺序模型的第一层中指定。

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()