Keras - "ValueError: Error when checking target: expected activation_1 to have shape (None, 9) but got array with shape (9,1)

Question

I'm building a model to classify text into one of 9 layers, and am having this error when running it. Activation 1 seems to refer to the Convolutional layer's input, but I'm unsure about what's wrong with the input.

num_classes=9
Y_train = keras.utils.to_categorical(Y_train, num_classes)
#Reshape data to add new dimension
X_train = X_train.reshape((100, 150, 1)) 
Y_train = Y_train.reshape((100, 9, 1)) 
model = Sequential() 
model.add(Conv1d(1, kernel_size=3, activation='relu', input_shape=(None, 1))) 
model.add(Dense(num_classes)) 
model.add(Activation('softmax')) 

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) 
model.fit(x=X_train,y=Y_train, epochs=200, batch_size=20)

Running this results in the following error:

"ValueError: Error when checking target: expected activation_1 to have shape (None, 9) but got array with shape (9,1)

Answer 1

There are several typos and bugs in your code.

1. Y_train = Y_train.reshape((100,9))

2. Since you reshape X_train to (100,150,1), I guess your input step is 150, and channel is 1. So for the Conv1D, (there is a typo in your code), input_shape=(150,1).

3. You need to flatten your output of conv1d before feeding into Dense layer.

import keras
from keras import Sequential
from keras.layers import Conv1D, Dense, Flatten

X_train = np.random.normal(size=(100,150))
Y_train = np.random.randint(0,9,size=100)

num_classes=9
Y_train = keras.utils.to_categorical(Y_train, num_classes)
#Reshape data to add new dimension
X_train = X_train.reshape((100, 150, 1)) 
Y_train = Y_train.reshape((100, 9)) 
model = Sequential() 
model.add(Conv1D(2, kernel_size=3, activation='relu', input_shape=(150,1)))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax')) 

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) 
model.fit(x=X_train,y=Y_train, epochs=200, batch_size=20)