ValueError：logits 和标签必须具有相同的形状 ((1, 7, 7, 2) vs (1, 2))

Question

I'm quite new to CNN. I'm trying to create a the following model. but I get the following error: "ValueError: logits and labels must have the same shape ((1, 7, 7, 2) vs (1, 2))"

Below the code I'm trying to implement

#create the training data set
train_data=scaled_data[0:training_data_len,:]
#define the number of periods
n_periods=28
#split the data into x_train and y_train data set
x_train=[]
y_train=[]

for i in range(n_periods,len(train_data)):
    x_train.append(train_data[i-n_periods:i,:28])
    y_train.append(train_data[i,29])

x_train=np.array(x_train)
y_train=np.array(y_train)

#Reshape the train data
x_train=x_train.reshape(x_train.shape[0],x_train.shape[1],x_train.shape[2],1)
x_train.shape
y_train = keras.utils.to_categorical(y_train,2)

# x_train as the folllowing shape (3561, 28, 28, 1)
# y_train as the following shape (3561, 2, 2)

#Build the 2 D CNN model for regression
model= Sequential()
model.add(Conv2D(32,kernel_size=(3,3),padding='same',activation='relu',input_shape=(x_train.shape[1],x_train.shape[2],1)))
model.add(Conv2D(64,kernel_size=(3,3),padding='same',activation='relu'))
model.add(MaxPooling2D(pool_size=(4,4)))
model.add(Dropout(0.25))
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='sigmoid'))
model.add(Dense(2, activation='sigmoid'))
model.summary()

#compile the model
model.compile(optimizer='ADADELTA', loss='binary_crossentropy', metrics=['accuracy'])

#train the model

model.fit(x_train, y_train, batch_size=1, epochs=1, verbose=2)

Answer 1

There are two problems in your approach:

1. You're using Convolutional/MaxPooling layers in which the inputs/outputs are as matrices, ie, with the shape of (Batch_Size, Height, Width, Depth). You then add some Dense layers which usually expect vectors, not matrices as inputs. Therefore, you have to first flatten the outputs of MaxPooling before giving it to Dense layer, ie, add a model.add(Flatten()) after model.add(Dropout(0.25)) and before model.add(Dense(128,activation='relu')) .
2. You are doing binary classification, ie, you have two classes. You are using binary_crossentropy as the loss function, for this to work, you should keep your targets as they are ( 0 and 1 ) and not use y_train = keras.utils.to_categorical(y_train,2) . Your final layer should have 1 neuron and not 2 (Change model.add(Dense(2, activation='sigmoid')) into model.add(Dense(1, activation='sigmoid')) )