The sequential model is like a linear stack of layers. It is useful for building simple models, such as the simple classification network and encoder-decoder models. It basically treats the layer as an object that is fed into the next layer.
For most problems, the sequential API lets you create layer-by-layer models. It restricts us from creating models that share layers or have multiple inputs or outputs.
Let's look at the Python code for this:
- Let's begin by importing the key Python libraries:
In[1]: import tensorflow as tf
In[2]: from tensorflow import keras
In[3]: from tensorflow.keras import layers
- We will define the model as a sequential model (In[4]) and then add a flatten layer. With the hidden layer, we have 120 neurons (In[6], In[7]), and the activation function is Rectified Linear Units (ReLU). In[8] is the last layer as it has 10 neurons and a softmax function; it turns logits into probabilities that sum to one:
In[4]: model = tf.keras...