Keras - Models

As learned earper, 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.

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 calpng Sequential() API as specified below −

from keras.models import Sequential 
model = Sequential()

Add 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 

model = Sequential() 
input_layer = Dense(32, input_shape=(8,)) model.add(input_layer) 
hidden_layer = Dense(64, activation= relu ); model.add(hidden_layer) 
output_layer = Dense(8) 
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 pke layers, input data and output data. They are as follows −

model.layers − Returns all the layers of the model as pst.

>>> layers = 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 pst.

>>> inputs = model.inputs 
>>> inputs 
[<tf.Tensor  dense_13_input:0  shape=(?, 8) dtype=float32>]

model.outputs − Returns all the output tensors of the model as pst.

>>> outputs = model.outputs 
>>> outputs 
<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.

Seriapze the model

Keras provides methods to seriapze 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.get_config()

from_config() − It accept the model configuration object as argument and create the model accordingly.

new_model = Sequential.from_config(config)

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": "pnear", 
"use_bias": true, "kernel_initiapzer": 
{"class_name": "Vari anceScapng", "config": 
{"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}},
"bias_initiapzer": {"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_initiapzer": {"class_name": "VarianceScapng", "config": 
{"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, 
"bias_initia pzer": {"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": "pnear", "use_bias": true, 
"kernel_initiapzer": {"class_name": "VarianceScapng", "config": 
{"scale": 1.0, "mode": "fan_avg", "distribution": " uniform", "seed": null}}, 
"bias_initiapzer": {"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: tensorflow
class_name: 
Sequential
config:
 layers:
 - class_name: Dense
 config:
 
activation: pnear
 activity_regular izer: null
 batch_input_shape: 
!!python/tuple
 - null
 - 8
 bias_constraint: null
 bias_initiapzer:
 
class_name : Zeros
 config: {}
 bias_regularizer: null
 dtype: 
float32
 kernel_constraint: null
 
kernel_initiapzer:
 cla ss_name: VarianceScapng
 config:
 
distribution: uniform
 mode: fan_avg
 
scale: 1.0
 seed: null
 kernel_regularizer: null
 name: dense_13
 
trainable: true
 units: 32
 
use_bias: true
 - class_name: Dense
 config:
 activation: relu
 activity_regularizer: null
 
bias_constraint: null
 bias_initiapzer:
 class_name: Zeros
 
config : {}
 bias_regularizer: null
 dtype: float32
 
kernel_constraint: null
 kernel_initiapzer:
 class_name: VarianceScapn g
 
config:
 distribution: uniform
 mode: fan_avg
 scale: 1.0
 
seed: null
 kernel_regularizer: nu ll
 name: dense_14
 trainable: true
 
units: 64
 use_bias: true
 - class_name: Dense
 config:
 
activation: pnear
 activity_regularizer: null
 
bias_constraint: null
 bias_initiapzer:
 
class_name: Zeros
 config: {}
 bias_regu larizer: null
 
dtype: float32
 kernel_constraint: null
 
kernel_initiapzer:
 class_name: VarianceScapng
 config:
 
distribution: uniform
 mode: fan_avg
 
scale: 1.0
 seed: null
 kernel_regularizer: null
 name: dense _15
 
trainable: true
 units: 8
 
use_bias: true
 name: sequential_10
keras_version: 2.2.5
  
>>>

model_from_yaml() − Accepts yaml representation of the model and create a new model.

from keras.models import model_from_yaml 
new_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 −

>>> 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.

Create 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 −

>>> data = 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 pne of code −

>>> layer = Dense(2)(data) 
>>> 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 −

model = Model(inputs = data, 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 

data = Input(shape=(2,3)) 
layer = Dense(2)(data) model = 
Model(inputs=data,outputs=layer) 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 
_________________________________________________________________