ValueError:层conv2d_10的输入0与层不兼容:应为ndim=4，实际为ndim=3.收到完整形状:[无，

Question

所以我一直在学习一个关于机器学习的教程，我在代码中已经达到了这一点：

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,Activation, Flatten, Conv2D, MaxPooling2D
import pickle
import numpy as np
pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
X=np.array(X/255.0)
y=np.array(y)
model = Sequential()
model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(1))
model.add(Activation("sigmoid"))
model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"])
model.fit(X,y, batch_size=32, validation_split=0.1)

当我执行此代码时，它会给我以下错误：ValueError: Input 0 of layer conv2d_10 is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [None, 100, 100]我看过很多关于这方面的帖子，但没有一个真正帮助过我！有人能帮忙吗？？提前感谢！！：(

Answer 1

添加一个整形，因为conv2D层需要(batch, x, y, channels)(ndim=4(，但您只提供了(batch, x, y)(ndim=3(。只需将其重塑为(batch, x, y, 1)即可。

读取Full shape received: [None, 100, 100]时出错。它所期望的是一个4D阵列[None, 100, 100, 1]-

model = Sequential()
model.add(Reshape((100,100,1),input_shape=X.shape[1:]))
model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(1))
model.add(Activation("sigmoid"))
model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"])

model.summary()

Model: "sequential_5"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
reshape_5 (Reshape)          (None, 100, 100, 1)       0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 98, 98, 64)        640       
_________________________________________________________________
activation_9 (Activation)    (None, 98, 98, 64)        0         
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 49, 49, 64)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 47, 47, 64)        36928     
_________________________________________________________________
activation_10 (Activation)   (None, 47, 47, 64)        0         
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 23, 23, 64)        0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 33856)             0         
_________________________________________________________________
dense_6 (Dense)              (None, 64)                2166848   
_________________________________________________________________
dense_7 (Dense)              (None, 1)                 65        
_________________________________________________________________
activation_11 (Activation)   (None, 1)                 0         
=================================================================
Total params: 2,204,481
Trainable params: 2,204,481
Non-trainable params: 0
_________________________________________________________________