ValueError：对于输入形状为 \[?,1,1,64\] 的 'max_pooling2d_6/MaxPool' (op: 'MaxPool') 从 1 中减去 2 导致的负维度大小

Question

当我将输入图像的高度和宽度保持在 362X362 以下时，出现负尺寸错误。我很惊讶，因为这个错误通常是由于错误的输入尺寸引起的。我没有找到任何数字或行和列会导致错误的原因。下面是我的代码-

 batch_size = 32
num_classes = 7
epochs=50
height = 362
width = 362

train_datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    'train',
        target_size=(height, width),
        batch_size=batch_size,
        class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
     'validation',
        target_size=(height, width),
        batch_size=batch_size,
        class_mode='categorical')

base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=
(height,width,3))

x = base_model.output
x = Conv2D(32, (3, 3), use_bias=True, activation='relu') (x) #line2
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(64, (3, 3), activation='relu') (x) #line3
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dense(batch_size, activation='relu')(x) #line1
x = (Dropout(0.5))(x)
predictions = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

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

model.fit_generator(
        train_generator,
        samples_per_epoch=128,
        nb_epoch=epochs,
        validation_data=validation_generator,
        verbose=2)

for i, layer in enumerate(base_model.layers):
    print(i, layer.name)

for layer in model.layers[:309]:
    layer.trainable = False
for layer in model.layers[309:]:
    layer.trainable = True

from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
loss='categorical_crossentropy', metrics=['accuracy'])

model.save('my_model.h5')
model.fit_generator(
        train_generator,
        samples_per_epoch=512,
        nb_epoch=epochs,
        validation_data=validation_generator,
        verbose=2)

原文由 aaaaa 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

替换这个：

 x = MaxPooling2D(pool_size=(2, 2))(x)

有了这个：

 x = MaxPooling2D((2,2), padding='same')(x)

以防止下采样期间的维度。

原文由 Dr Sudeep Ghosh 发布，翻译遵循 CC BY-SA 4.0 许可协议