Keras -About Models

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Keras -About Models
11 Mar

Keras -About Models

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Learn Keras

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As learned earlier, Keras model represents the actual neural network model. Keras provides a two mode to create the model, simple and easy to use Sequential API as well as more flexible and advanced Functional API. Let us learn now to create model using both Sequential and Functional API in this chapter.

About Sequential

The core idea of Sequential API is simply arranging the Keras layers in a sequential order and so, it is called Sequential API. Most of the ANN also has layers in sequential order and the data flows from one layer to another layer in the given order until the data finally reaches the output layer.
A ANN model can be created by simply calling Sequential() API as specified below −

from keras.models import Sequential
my_model = Sequential()

Adding layers

To add a layer, simply create a layer using Keras layer API and then pass the layer through add() function as specified below −

from keras.models import Sequential
my_model = Sequential()
input_layer = Dense(32, input_shape=(8,)) my_model.add(input_layer)
hidden_layer = Dense(64, activation='relu'); my_model.add(hidden_layer)
output_layer = Dense(8)
my_model.add(output_layer)

Here, we have created one input layer, one hidden layer and one output layer.

Access the model

Keras provides few methods to get the model information like layers, input data and output data. They are as follows −

  • model.layers − Returns all the layers of the model as list. 
    >>> layers = my_model.layers
>>> layers
[
<keras.layers.core.Dense object at 0x000002C8C888B8D0>,
<keras.layers.core.Dense object at 0x000002C8C888B7B8>
<keras.layers.core.Dense object at 0x 000002C8C888B898>
]
  • model.inputs − Returns all the input tensors of the model as list. 
    >>> inputs_data = my_model.inputs
>>> inputs_data
[<tf.Tensor 'dense_13_input:0' shape=(?, 8) dtype=float32>]
  • model.outputs − Returns all the output tensors of the model as list. 
    >>> outputs_data = my_model.outputs
>>> outputs_data
<tf.Tensor 'dense_15/BiasAdd:0' shape=(?, 8) dtype=float32>]
  • model.get_weights − Returns all the weights as NumPy arrays.
  • model.set_weights(weight_numpy_array) − Set the weights of the model.

    Serialize the model

    Keras provides methods to serialize the model into object as well as json and load it again later. They are as follows −

  • get_config() − IReturns the model as an object. 
    config_model = my_model.get_config()
  • from_config() − It accept the model configuration object as argument and create the model accordingly. 
    new_model = Sequential.from_config(config_model)
  • to_json() − Returns the model as an json object. 
    >>> json_string = model.to_json()
>>> json_string '{"class_name": "Sequential", "config":
{"name": "sequential_10", "layers":
[{"class_name": "Dense", "config":
{"name": "dense_13", "trainable": true, "batch_input_shape":
[null, 8], "dtype": "float32", "units": 32, "activation": "linear",
"use_bias": true, "kernel_initializer":
{"class_name": "Vari anceScaling", "config":
{"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}},
"bias_initializer": {"class_name": "Zeros", "conf
ig": {}}, "kernel_regularizer": null, "bias_regularizer": null,
"activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}},
{" class_name": "Dense", "config": {"name": "dense_14", "trainable": true,
"dtype": "float32", "units": 64, "activation": "relu", "use_bias": true,
"kern el_initializer": {"class_name": "VarianceScaling", "config":
{"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}},
"bias_initia lizer": {"class_name": "Zeros",
"config": {}}, "kernel_regularizer": null, "bias_regularizer": null,
"activity_regularizer": null, "kernel_constraint" : null, "bias_constraint": null}},
{"class_name": "Dense", "config": {"name": "dense_15", "trainable": true,
"dtype": "float32", "units": 8, "activation": "linear", "use_bias": true,
"kernel_initializer": {"class_name": "VarianceScaling", "config":
{"scale": 1.0, "mode": "fan_avg", "distribution": " uniform", "seed": null}},
"bias_initializer": {"class_name": "Zeros", "config": {}},
"kernel_regularizer": null, "bias_regularizer": null, "activity_r egularizer":
null, "kernel_constraint": null, "bias_constraint":
null}}]}, "keras_version": "2.2.5", "backend": "tensorflow"}'
>>>
  • model_from_json() − Accepts json representation of the model and create a new model. 
    from keras.models import model_from_json
new_model = model_from_json(json_string)
  • to_yaml() − Returns the model as a yaml string. 
    >>> yaml_string = model.to_yaml()
>>> yaml_string 'backend: tensorflownclass_name:
Sequentialnconfig:n layers:n - class_name: Densen config:n
activation: linearn activity_regular izer: nulln batch_input_shape:
!!python/tuplen - nulln - 8n bias_constraint: nulln bias_initializer:n
class_name : Zerosn config: {}n bias_regularizer: nulln dtype:
float32n kernel_constraint: nulln
kernel_initializer:n cla ss_name: VarianceScalingn config:n
distribution: uniformn mode: fan_avgn
scale: 1.0n seed: nulln kernel_regularizer: nulln name: dense_13n
trainable: truen units: 32n
use_bias: truen - class_name: Densen config:n activation: relun activity_regularizer: nulln
bias_constraint: nulln bias_initializer:n class_name: Zerosn
config : {}n bias_regularizer: nulln dtype: float32n
kernel_constraint: nulln kernel_initializer:n class_name: VarianceScalin gn
config:n distribution: uniformn mode: fan_avgn scale: 1.0n
seed: nulln kernel_regularizer: nu lln name: dense_14n trainable: truen
units: 64n use_bias: truen - class_name: Densen config:n
activation: linearn activity_regularizer: nulln
bias_constraint: nulln bias_initializer:n
class_name: Zerosn config: {}n bias_regu larizer: nulln
dtype: float32n kernel_constraint: nulln
kernel_initializer:n class_name: VarianceScalingn config:n
distribution: uniformn mode: fan_avgn
scale: 1.0n seed: nulln kernel_regularizer: nulln name: dense _15n
trainable: truen units: 8n
use_bias: truen name: sequential_10nkeras_version: 2.2.5n'
>>>
  • model_from_yaml() − Accepts yaml representation of the model and create a new model. 
    from keras.models import model_from_yaml
new_my_model = model_from_yaml(yaml_string)

    Summarise the model

    Understanding the model is very important phase to properly use it for training and prediction purposes. Keras provides a simple method, summary to get the full information about the model and its layers.
    A summary of the model created in the previous section is as follows −

    >>> new_my_model.summary() Model: "sequential_10"
_________________________________________________________________
Layer (type) Output Shape Param
#================================================================
dense_13 (Dense) (None, 32) 288
_________________________________________________________________
dense_14 (Dense) (None, 64) 2112
_________________________________________________________________
dense_15 (Dense) (None, 8) 520
=================================================================
Total params: 2,920
Trainable params: 2,920
Non-trainable params: 0
_________________________________________________________________
>>>

    Train and Predict the model

    Model provides function for training, evaluation and prediction process. They are as follows −

  • compile − Configure the learning process of the model
  • fit − Train the model using the training data
  • evaluate − Evaluate the model using the test data
  • predict − Predict the results for new input.

    Functional API

    Sequential API is used to create models layer-by-layer. Functional API is an alternative approach of creating more complex models. Functional model, you can define multiple input or output that share layers. First, we create an instance for model and connecting to the layers to access input and output to the model. This section explains about functional model in brief.

    Creating a model

    Import an input layer using the below module −

    >>> from keras.layers import Input

    Now, create an input layer specifying input dimension shape for the model using the below code −

    >>> dataset = Input(shape=(2,3))

    Define layer for the input using the below module −

    >>> from keras.layers import Dense

    Add Dense layer for the input using the below line of code −

    >>> layer = Dense(2)(dataset)
>>> print(layer)
Tensor("dense_1/add:0", shape =(?, 2, 2), dtype = float32)

    Define model using the below module −

    from keras.models import Model

    Create a model in functional way by specifying both input and output layer −

    my_model = Model(inputs = dataset, outputs = layer)

    The complete code to create a simple model is shown below −

    from keras.layers import Input
from keras.models import Model
from keras.layers import Dense
dataset = Input(shape=(2,3))
layer = Dense(2)(dataset) my_model =
Model(inputs=dataset,outputs=layer)
my_model.summary()
_________________________________________________________________
Layer (type)               Output Shape               Param #
=================================================================
input_2 (InputLayer)       (None, 2, 3)               0
_________________________________________________________________
dense_2 (Dense)            (None, 2, 2)               8
=================================================================
Total params: 8
Trainable params: 8
Non-trainable params: 0
_________________________________________________________________
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