Keras输入形状抛出预期为4d的值错误，但得到一个形状为数组(60000，28,28) [英] Keras input shape throws value error expected 4d but got an array with shape (60000, 28,28)

问题描述

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

x_train.shape #Shape is (60000, 28, 28)

然后该模型确保输入形状为28,28,1，因为60k是样本.

Then the model made sure input shape is 28,28,1 since 60k is the sample.

model2 = tf.keras.Sequential()
# Must define the input shape in the first layer of the neural network
model2.add(tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1))) 
model2.add(tf.keras.layers.MaxPooling2D(pool_size=2))
model2.add(tf.keras.layers.Dropout(0.3))
model2.add(tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model2.add(tf.keras.layers.MaxPooling2D(pool_size=2))
model2.add(tf.keras.layers.Dropout(0.3))
model2.add(tf.keras.layers.Flatten())
model2.add(tf.keras.layers.Dense(256, activation='relu'))
model2.add(tf.keras.layers.Dropout(0.5))
model2.add(tf.keras.layers.Dense(10, activation='softmax'))
model2.compile(loss='categorical_crossentropy',
             optimizer='adam',
             metrics=['accuracy'])
model2.fit(x_train,
         y_train,
         batch_size=64,
         epochs=25,)

我得到了错误: ValueError:检查输入时出错:预期conv2d_19_input具有4维，但数组的形状为(60000，28，28)

I get the Error: ValueError: Error when checking input: expected conv2d_19_input to have 4 dimensions, but got array with shape (60000, 28, 28)

就像每次我尝试理解输入形状一样，我都会感到更加困惑.就像im与conv2d的输入形状混淆了，此时稠密. 无论如何，为什么会这样?

Like everytime i try to understand input shape i get more confused. Like im confused with the input shapes for conv2d and dense at this point. Anyway, Why is this wrong?

推荐答案

是的，这是正确的，参数input_shape准备采用3个值.但是，功能Conv2D希望将4D数组作为输入，内容包括:

Yes, this is correct the parameter input_shape is prepared to take 3 values. However the function Conv2D is expecting a 4D array as input, covering:

1. 样本数
2. 频道数
3. 图像宽度
4. 图像高度

函数load_data()是一个3D数组，由宽度，高度和样本数组成.

Whereas the function load_data() is a 3D array consisting of width, height and number of samples.

您可以期望通过简单的重塑来解决该问题:

You can expect to solve the issue with a simple reshape:

train_X = train_X.reshape(-1, 28,28, 1)
test_X = test_X.reshape(-1, 28,28, 1)

来自keras文档的更好信息:

A better defitinion from keras documentation:

输入形状: 如果data_format为"channels_first"，则形状为(批，行，列，列)的4D张量；如果da​​ta_format为"channels_last"，则形状为:(批，行，列，列)的4D张量.

Input shape: 4D tensor with shape: (batch, channels, rows, cols) if data_format is "channels_first" or 4D tensor with shape: (batch, rows, cols, channels) if data_format is "channels_last".

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