ValueError：层顺序的输入0与层不兼容：：预期的min_ndim = 4，发现ndim = 2。收到的完整形状：（无，1024）

Question

这里是新的 Python 开发人员。我在这里查看了其他类似的帖子，但我无法正确理解。将不胜感激。

print('X_train:', X_train.shape)
print('y_train:', y_train1.shape)
print('X_test:', X_train.shape)
print('y_test:', y_train1.shape)

X_train: (42000, 32, 32) y_train: (42000,) X_test: (42000, 32, 32) y_test: (42000,)

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
def featuremodel() :
    model = Sequential()
    model.add(Conv2D(32, kernel_size=4, activation='relu', input_shape=(X_train.shape[0],32,64)))
    model.add(MaxPooling2D(pool_size=3))
    model.add(Conv2D(64, kernel_size=4, activation='relu'))
    model.add(Flatten())
    model.add(Dense(len(y_train[0]), activation='softmax'))
    model.compile(loss='categorical_crossentropy',
          optimizer='adadelta',      
          metrics=['acc'])
    model.summary()
    model.fit(X_train, y_train, epochs = 10, validation_data = (X_test,y_test))

返回模型

ValueError：layersequential_7 的输入 0 与 layer 不兼容：：预期 min_ndim=4，发现 ndim=2。收到的完整形状：（无，1024）

Answer 1

您指定的输入形状应更改。您的输入有 42000 个样本，每个样本都有 (32,32) 形状。您不应该将样本数量(42000) 传递给输入层，并且应该添加通道维度。所以输入的形状应该是(32,32,1)。

修改后的代码应该是这样的：

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D

# test data
X_train = tf.random.uniform((42000,32,32))  
y_train1 = tf.random.uniform((42000,))

X_train = tf.expand_dims(X_train, axis=3)    #add channel axis (42000,32,32) => (42000,32,32,1) 

model = Sequential()
model.add(Conv2D(32, kernel_size=4, activation='relu', input_shape=(32,32,1))) #change input shape
model.add(MaxPooling2D(pool_size=3))
model.add(Conv2D(64, kernel_size=4, activation='relu'))
model.add(Flatten())
#last layer should have output like your y data. in this case it should be 1, since you have 1 y for each sample
model.add(Dense(1, activation='softmax'))  
model.compile(loss='categorical_crossentropy',
      optimizer='adadelta',      
      metrics=['acc'])
model.summary()
model.fit(X_train, y_train1, epochs = 10) #, validation_data = (X_test,y_test))