InvalidArgumentError:输入深度必须被过滤器深度均匀整除:3 vs 6

Question

input_shape=(100,100,6)
input_tensor=keras.Input(input_shape)

model.add(Conv2D(32, 3, padding='same', activation='relu', input_shape=input_shape))
model.add((Conv1D(filters=32, kernel_size=2, activation='relu', padding='same')))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(64, 3, padding='same', activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Conv2D(128, 3, padding='same', activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=['accuracy'])
training_set = train_datagen.flow_from_directory('/content/gdrive/My Drive/Data/training_set',
target_size = (128, 128),
batch_size = 32,
class_mode = 'categorical')
history=model.fit(training_set,
steps_per_epoch=nb_train_images//batch_size,
epochs=100,
validation_data=test_set,
validation_steps=nb_test_images//batch_size,
callbacks=callbacks)
history=model.fit(training_set,
steps_per_epoch=nb_train_images//batch_size,
epochs=40,
validation_data=test_set,
validation_steps=nb_test_images//batch_size,
callbacks=callbacks)

我有6种不同类型的集合要分类。我哪里做错了?我已经在上面添加了输入形状，我提到1001006，有人能帮助理解这个问题吗?

Answer 1

这也发生在我身上。以下是我的代码。我修复它的方式是，而不是有一些不同的输入形状，我只是使输入形状的训练数据的图像形状。

train = image_gen.flow_from_directory(
train_path,
target_size=(500, 500
),
color_mode='grayscale',
class_mode='binary',
batch_size=16
)
#and then later, when I build the model
model.add(Conv2D(filters[0], (5, 5),  padding='same',kernel_regularizer=12(0.001), activation='relu', input_shape=train.image_shape))
#the important part is the input_shape=train.image_shape