image - ValueError: Input 0 of layer sequential is incompatible with the layer

Question

I am trying to run this model but I keep getting this error. There is some mistake with regard to the shape of input data, I played around with it but I still get these errors.

Error:

ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape (None, 32, 32, 3)

# Image size
img_width = 32
img_height = 32

# Define X as feature variable and Y as name of the class(label)
X = []
Y = []

for features,label in data_set:
    X.append(features)
    Y.append(label)

X = np.array(X).reshape(-1,img_width,img_height,3)
Y = np.array(Y)
print(X.shape)  # Output :(4943, 32, 32, 3)
print(Y.shape)  # Output :(4943,)

# Normalize the pixels
X = X/255.0

# Build the model
cnn = Sequential()
cnn.add(keras.Input(shape = (32,32,1)))
cnn.add(Conv2D(32, (3, 3), activation = "relu", input_shape = X.shape[1:]))
cnn.add(MaxPooling2D(pool_size = (2, 2)))
cnn.add(Conv2D(32, (3, 3), activation = "relu",input_shape = X.shape[1:]))
cnn.add(MaxPooling2D(pool_size = (2, 2)))
cnn.add(Conv2D(64, (3,3), activation = "relu",input_shape = X.shape[1:]))
cnn.add(MaxPooling2D(pool_size = (2,2)))
cnn.add(Flatten())
cnn.add(Dense(activation = "relu", units = 150))
cnn.add(Dense(activation = "relu", units = 50))
cnn.add(Dense(activation = "relu", units = 10))
cnn.add(Dense(activation = 'softmax', units = 1))
cnn.summary()
cnn.compile(loss = 'categorical_crossentropy',optimizer = 'adam',metrics = ['accuracy'])

# Model fit
cnn.fit(X, Y, epochs = 15)e

I tried reading about this issue, but still didn't understand it very well.

Answer 1

your input shape should be (32,32,3). y is your label matrix. I assume it contains N unique integer values where N is the number of classes. If N=2 you can treat this as a binary classification problem. In that case your code for the top layer should be

cnn.add(Dense(1, activation = 'sigmoid'))

your code for compile should be

cnn.compile(loss = 'binary_crossentropy',optimizer = 'adam',metrics = ['accuracy'])

If you have more than 2 classes then your code should be

cnn.add(Dense(N, activation = 'softmax'))
cnn.compile(loss = 'sparse_categorical_crossentropy',optimizer = 'adam',metrics = ['accuracy'])

Where N is the number of classes,