So for an application I'm making I'm using tf.keras.models.Sequential. I know that there are linear and multilinear regression models for machine learning. In the documentation of Sequential is said that the model is a linear stack of layers. Is that equal to multilinear regression? The only explaination of linear stack of layers I could find was this question on Stackoverflow.
def trainModel(bow,unitlabels,units):
x_train = np.array(bow)
print("X_train: ", x_train)
y_train = np.array(unitlabels)
print("Y_train: ", y_train)
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(256, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(len(units), activation=tf.nn.softmax)])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=50)
return model
you are confusing two things very important here. One is the model and the other is the model of the model.
The model of the model is indeed a linear one because it follows a direct line (straightforward) from beginning till end.
the model itself is not linear: The relu activation is here to make sure that the solutions are not linear.
the linear stack is not a linear regression nor a multilinear one. The linear stack is not a ML term here but the english one to say straightforward. tell me if i misunderstood the question in any regard.