Tensorflow Keras tutotrials01

tf.keras.Sequential

groups a linear stack Models into Sequential

Here are quick starts for the beginners and experts respectively: MINIST Recongnization

So, the key point of the programming is about how to build the Model. Here, we build tf.keras model using subclass tf.keras.Sequential.

Then，we talk about how to use Sequential model:

A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.

Schematically, the following Sequential model:

# Define Sequential model with 3 layers
model = keras.Sequential(
    [
        layers.Dense(2, activation="relu", name="layer1"),
        layers.Dense(3, activation="relu", name="layer2"),
        layers.Dense(4, name="layer3"),
    ]
)
# Call model on a test input
x = tf.ones((3, 3))
y = model(x)

Dense implements the operation: output = activation(dot(input, kernel) + bias) where activation is the element-wise activation function passed as the activation argument, kernel is a weights matrix created by the layer, and bias is a bias vector created by the layer (only applicable if use_bias is True).

Arguments
`units`	Positive integer, dimensionality of the output space.
`activation`	Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
`use_bias`	Boolean, whether the layer uses a bias vector.
`kernel_initializer`	Initializer for the `kernel` weights matrix.
`bias_initializer`	Initializer for the bias vector.
`kernel_regularizer`	Regularizer function applied to the `kernel` weights matrix.
`bias_regularizer`	Regularizer function applied to the bias vector.
`activity_regularizer`	Regularizer function applied to the output of the layer (its "activation").
`kernel_constraint`	Constraint function applied to the `kernel` weights matrix.
`bias_constraint`	Constraint function applied to the bias vector.

Input shape:

N-D tensor with shape: (batch_size, ..., input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim).

Output shape:

N-D tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units).

*relationship between tensorflow and keras