class_weights = {0: 1.4, 1: 1, 2: 2.3, 3: 2.3}

# sets up the model shape
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.05))

model.add(Dense(64, activation='relu'))
model.add(Dropout(0.05))

model.add(Dense(n_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='Adam',
              metrics=['accuracy'])

# sets up the call back so we can have the logs
tbCallBack = TensorBoard(log_dir=log_pth.format(time()))