I fixed that but then when i build the model and i fit it:
model = Sequential()

model.add(Conv2D(5, kernel_size=(5, 5),activation='linear',input_shape=(80,60,1),padding='same'))

model.add(Activation('relu'))

model.add(MaxPooling2D((2, 2),padding='same'))

model.add(Conv2D(5, (5, 5), activation='linear',padding='same'))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))


model.add(Conv2D(5, (5, 5), activation='linear',padding='same'))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))

model.add(Flatten())

model.add(Dense(100, activation='linear'))

model.add(Dense(1, activation='linear'))

#Compiling model


model.compile(

    optimizer=tf.keras.optimizers.Adam(0.001),

    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],

)
model.fit(train_ds, validation_data=valid_ds, batch_size=32, epochs=10)

I get this error:
ValueError: Input 0 of layer sequential_6 is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: (80, 60)