序列的输入层0与层- cnn不兼容

Question

我正在尝试使用超参数化创建一个CNN模型用于图像分类。当我运行代码时，我收到以下错误:

ValueError: Input 0 of layer "sequential"与层不兼容:expected shape=(None, 32,32,33,3)， found shape=(32,32,3)

如何修复错误?以下是粘贴在下面的完整代码:

# 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，您将获得此错误。

这里我在mnist fashion数据集上实现了超模型，该数据集包含形状(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，您将不会得到任何错误。

要了解更多细节，请参考本要点和本文档。谢谢你！