Error when checking target: expected softmax_1 to

问题:

I am using Keras for building Conv Net for the first time. My layers are as follows:

layers = [
Conv2D(8,kernel_size=(4,4),padding='same',input_shape=( 200, 180,3),kernel_initializer="glorot_normal",data_format="channels_first"),
Activation("relu"),
MaxPooling2D(pool_size=(8,8),padding='same',data_format='channels_first'),
Conv2D(16,(2,2),padding='same',kernel_initializer="glorot_normal"),
Activation("relu"),
MaxPooling2D(pool_size=(4,4),padding='same',data_format='channels_first'),
Conv2D(4,(3,3),padding='same',kernel_initializer="glorot_normal"),
Activation("relu"),
MaxPooling2D(pool_size=(2,2),padding='same',data_format='channels_first'),
Flatten(),
Dense(2,input_shape=(48,)),
Softmax(axis=-1)
]
#Edit, here is the part for compiling the model and fitting it
model = Sequential(layers)    

model.compile(optimizer="adam",loss="sparse_categorical_crossentropy" 
metrics=["accuracy"])
trainHistory = model.fit(x=X_train,y=Y_train,batch_size=3,epochs=1000)

My labels array is of shape (,2). But when I try to use fit on the model, it gives me the error that softmax_1 expected to have shape (1,). But I have clearly mentioned units of Dense as 2 and softmax returns output of the same dimension as the input.

So where did the 1, came from? I tried to use dummy label array of 1 dimension and it runs. So what am I doing wrong? How do I use 2 dimensional array that I have?

回答1:

The problem is that you are using sparse_categorical_crossentropy as the loss function. This loss function is used when the given labels (i.e. Y_train) are encoded as integers (i.e. 0, 1, 2, ...). However, If the labels are one-hot encoded, which seems to be the case in your code, you need to use categorical_crossentropy as the loss function instead.