序列的输入层 0 与层不兼容 - CNNs

Question

我正在尝试使用超参数化来创建 CNN 模型以进行图像分类。当我运行代码时，我收到以下错误：

ValueError: Input 0 of layer "sequential" is in compatible with the layer: expected shape=(None, 32, 32, 32, 3), found shape=(32, 32, 32, 3)

如何修复错误？这是下面粘贴的整个代码：

# first we create our actual code which requires the arguments, units, activation, dropout, lr:
def build_model(hp):
  model = ks.Sequential([
  # adding first conv2d layer
  ks.layers.Conv2D(
            #Let's tune the filters, kernel_size, activation function.
            filters = hp.Int("conv_1_filter", min_value=1,max_value=100, step = 16),
            kernel_size = hp.Choice("conv_1_kernel", values = [3,5]),
            activation = hp.Choice("conv_1_activation", ["relu", "tanh", "softmax"]),
            input_shape = (32,32,32,3) 
   ),
  # adding second conv2d layer
  ks.layers.Conv2D(
            #Let's tune the filters, kernel_size, activation function.
            filters = hp.Int("conv_2_filter", min_value=1,max_value=50, step = 16),
            kernel_size = hp.Choice("conv_2_kernel", values = [3,5]),
            activation = hp.Choice("conv_2_activation", ["relu", "tanh", "softmax"]),
            input_shape = (32,32,32,3)
  )])
  model.add(layers.Flatten())

  # Let's tune the number of Dense layers.
  for i in range(hp.Int("num_dense_layers", 1, 3)):
    model.add(
        layers.Dense(
            # Let's tune the number of units separately
            units = hp.Int(f"units_{i}", min_value=1, max_value = 100, step = 16),
            activation = hp.Choice("activation", ["relu", "tanh", "softmax"])
        ))

  if hp.Boolean("dropout"):
    model.add(layers.Dropout(rate = 0.25))

  model.add(layers.Dense(10, activation = "softmax"))
  learning_rate = hp.Float("lr", min_value = 1e-4, max_value = 1e-2, sampling="log")
  model.compile(
      optimizer = ks.optimizers.Adam(learning_rate = learning_rate),
      loss = "categorical_crossentropy",
      metrics = ["accuracy"]
  )  
  return model

  build_model(keras_tuner.HyperParameters())

Answer 1

由于输入形状不匹配，您会收到此错误。

在这里，我在 mnist 时尚数据集上实现了超模型，其中包含形状 (28,282,1) 的图像。

def build_model(hp):
  model = tf.keras.Sequential([
  tf.keras.Input(shape=(28,28,1)),
  # adding first conv2d layer
  tf.keras.layers.Conv2D(
            #Let's tune the filters, kernel_size, activation function.
            filters = hp.Int("conv_1_filter", min_value=1,max_value=100, step = 16),
            kernel_size = hp.Choice("conv_1_kernel", values = [3,5]),
            activation = hp.Choice("conv_1_activation", ["relu", "tanh", "softmax"]),
            input_shape = (28,28,1) 
   ),
  tf.keras.layers.MaxPooling2D(
      pool_size=hp.Choice('pooling_1',values=[2,3])),

  # adding second conv2d layer
  tf.keras.layers.Conv2D(
            #Let's tune the filters, kernel_size, activation function.
            filters = hp.Int("conv_2_filter", min_value=1,max_value=50, step = 16),
            kernel_size = hp.Choice("conv_2_kernel", values = [3,5]),
            activation = hp.Choice("conv_2_activation", ["relu", "tanh", "softmax"]),
            input_shape = (28,28,1)
  )])
  tf.keras.layers.MaxPooling2D(
      pool_size=hp.Choice('pooling_2',values=[2,3])),

  model.add(tf.keras.layers.Flatten())

  if hp.Boolean("dropout"):
    model.add(tf.keras.layers.Dropout(rate = 0.25))

  model.add(tf.keras.layers.Dense(10, activation = "softmax"))
  learning_rate = hp.Float("lr", min_value = 1e-4, max_value = 1e-2, sampling="log")
  model.compile(
      optimizer = tf.keras.optimizers.Adam(learning_rate = learning_rate),
      loss = "categorical_crossentropy",
      metrics = ["accuracy"]
  )  
  return model

通过提供正确的形状，您不会得到任何错误。

更多详情，请参考gist和documentation。谢谢！