Tensorflow 说层 conv2d 的 Input 0 与层不兼容：expected ndim=4, found ndim=3答案

【问题标题】：Tensorflow says Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=3Tensorflow 说层 conv2d 的 Input 0 与层不兼容：expected ndim=4, found ndim=3
【发布时间】：2020-02-24 18:51:36
【问题描述】：

这是我的代码：

(x_train, y_train), (x_test, y_test) = mnist.load_data()

def create_model():
    model = tf.keras.models.Sequential()

    model.add(Conv2D(64, (3, 3), input_shape=x_train.shape[1:], activation='relu'))
    model.add(MaxPooling2D(pool_size=2))

    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=2))

    model.add(Flatten())
    model.add(Dense(1024, activation='relu'))
    model.add(Dense(10, activation='softmax'))

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    return model

model = create_model()

输入数据形状为 (60000, 28, 28)。它是 keras mnist 数据集。这是错误

ValueError: Input 0 of layer conv2d_1 is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [None, 28, 28]

我不知道它有什么问题。

【问题讨论】：

标签： python-3.x tensorflow deep-learning conv-neural-network

【解决方案1】：

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".

输入形状应为 (batch,channels,rows,cols) 您给定的图像数量。

创建一个变量，如image_size=(3,28,28) 和

input_shape = image_size

...这可能对您有用。或者试试

input_shape = (3,28,28)

【讨论】：

【解决方案2】：

我意识到我的错误 mnist 数据有一个形状：(sample, width, height) 和 Conv2D 层需要一个形状 (samples, width, height, depth)，所以解决方案是添加一个额外的维度。

x_train = x_train[..., np.newaxis]
x_test = x_test[..., np.newaxis]

【讨论】：