Question

我一直试图在MNIST上使用keras.preprocessing.image.ImageDataGenerator()作为额外的例子。

我在Keras的Keras上使用了一个完全连接的网络。我首先在构建和编译模型之前将3D MNIST重塑为4D张量，然后使用数据增强来帮助拟合模型。

X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)


model = Sequential(name="mlp")

model.add(ll.InputLayer([28, 28]))
model.add(ll.Flatten())

model.add(ll.Dense(512, kernel_regularizer=regularizers.l2(0.04)))
model.add(ll.Activation('relu'))

model.add(ll.Dense(512, kernel_regularizer=regularizers.l2(0.04)))
model.add(ll.Activation('relu'))


model.add(ll.Dense(256, kernel_regularizer=regularizers.l2(0.04)))
model.add(ll.Activation('relu'))

model.add(ll.Dense(128, kernel_regularizer=regularizers.l2(0.04)))
model.add(ll.Activation('relu'))

model.add(ll.Dense(32, kernel_regularizer=regularizers.l2(0.04)))
model.add(ll.Activation('relu'))

model.add(ll.Dense(10, activation='softmax'))

model.compile("adam", "categorical_crossentropy", metrics=["accuracy"])


gen = ImageDataGenerator(rotation_range=8, width_shift_range=0.08, shear_range=0.3,
                         height_shift_range=0.08, zoom_range=0.08)

test_gen = ImageDataGenerator()
train_generator = gen.flow(X_train, y_train, batch_size=64)
test_generator = test_gen.flow(X_test, y_test, batch_size=64)

...
model.fit_generator(train_generator, steps_per_epoch=60000//64, epochs=5, 
                    validation_data=test_generator, validation_steps=10000//64)

我收到此错误：

  5 model.fit_generator(train_generator, steps_per_epoch=60000//64, epochs=5, 
    ----> 6                     validation_data=test_generator, validation_steps=10000//64)

ValueError: Error when checking input: expected input_6 to have 3 dimensions, but got array with shape (256, 28, 28, 1)

但是当我省略它转换为4d张量时，会发生这种情况：

     52 test_gen = ImageDataGenerator()
    ---> 53 train_generator = gen.flow(X_train, y_train, batch_size=64)
         54 test_generator = test_gen.flow(X_test, y_test, batch_size=64)

ValueError: ('Input data in `NumpyArrayIterator` should have rank 4. You passed an array with shape', (50000, 28, 28))

似乎列车和测试生成器对象生成4D张量，但模型本身需要3D张量。

Answer 1

当您的模型（输入图层）期待(batch, 28, 28, 1)时，您的数据为(batch, 28, 28)。

解决这个问题就像更改模型的输入形状一样简单：

#you don't need to add an input layer, actually, just pass the shape to the first layer:

 model = Sequential(name="mlp")
 model.add(ll.Flatten(input_shape=(28,28,1)))

Keras ImageDataGenerator（）一次期待3D数据和4级张量？

1 个答案: