A sequential model can be created using the ‘Sequential’ API that uses the ‘ layers.experimental.preprocessing.Rescaling’ method. The other layers are specified while created the model.
We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
print("Sequential model is being created") num_classes = 5 model = Sequential([ layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)), layers.Conv2D(16, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Conv2D(32, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Conv2D(64, 3, padding='same', activation='relu'), layers.MaxPooling2D(), layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(num_classes) ])
Code credit: https://www.tensorflow.org/tutorials/images/classification
Sequential model is being created