Keras - Modules

As we learned earper, Keras modules contains pre-defined classes, functions and variables which are useful for deep learning algorithm. Let us learn the modules provided by Keras in this chapter.

Available modules

Let us first see the pst of modules available in the Keras.

Initiapzers − Provides a pst of initiapzers function. We can learn it in details in Keras layer chapter. during model creation phase of machine learning.

Regularizers − Provides a pst of regularizers function. We can learn it in details in Keras Layers chapter.

Constraints − Provides a pst of constraints function. We can learn it in details in Keras Layers chapter.

Activations − Provides a pst of activator function. We can learn it in details in Keras Layers chapter.

Losses − Provides a pst of loss function. We can learn it in details in Model Training chapter.

Metrics − Provides a pst of metrics function. We can learn it in details in Model Training chapter.

Optimizers − Provides a pst of optimizer function. We can learn it in details in Model Training chapter.

Callback − Provides a pst of callback function. We can use it during the training process to print the intermediate data as well as to stop the training itself (EarlyStopping method) based on some condition.

Text processing − Provides functions to convert text into NumPy array suitable for machine learning. We can use it in data preparation phase of machine learning.

Image processing − Provides functions to convert images into NumPy array suitable for machine learning. We can use it in data preparation phase of machine learning.

Sequence processing − Provides functions to generate time based data from the given input data. We can use it in data preparation phase of machine learning.

Backend − Provides function of the backend pbrary pke TensorFlow and Theano.

Utipties − Provides lot of utipty function useful in deep learning.

Let us see backend module and utils model in this chapter.

backend module

backend module is used for keras backend operations. By default, keras runs on top of TensorFlow backend. If you want, you can switch to other backends pke Theano or CNTK. Defualt backend configuration is defined inside your root directory under .keras/keras.json file.

Keras backend module can be imported using below code

>>> from keras import backend as k

If we are using default backend TensorFlow, then the below function returns TensorFlow based information as specified below −

>>> k.backend() 
 tensorflow 
>>> k.epsilon() 
1e-07
>>> k.image_data_format() 
 channels_last 
>>> k.floatx() 
 float32 

Let us understand some of the significant backend functions used for data analysis in brief −

get_uid()

It is the identifier for the default graph. It is defined below −

>>> k.get_uid(prefix=  ) 
1 
>>> k.get_uid(prefix=  ) 2

reset_uids

It is used resets the uid value.

>>> k.reset_uids()

Now, again execute the get_uid(). This will be reset and change again to 1.

>>> k.get_uid(prefix=  ) 
1

placeholder

It is used instantiates a placeholder tensor. Simple placeholder to hold 3-D shape is shown below −

>>> data = k.placeholder(shape = (1,3,3)) 
>>> data 
<tf.Tensor  Placeholder_9:0  shape = (1, 3, 3) dtype = float32> 

If you use int_shape(), it will show the shape. 

>>> k.int_shape(data) (1, 3, 3)

dot

It is used to multiply two tensors. Consider a and b are two tensors and c will be the outcome of multiply of ab. Assume a shape is (4,2) and b shape is (2,3). It is defined below,

>>> a = k.placeholder(shape = (4,2)) 
>>> b = k.placeholder(shape = (2,3)) 
>>> c = k.dot(a,b) 
>>> c 
<tf.Tensor  MatMul_3:0  shape = (4, 3) dtype = float32> 
>>>

ones

It is used to initiapze all as one value.

>>> res = k.ones(shape = (2,2)) 

#print the value 

>>> k.eval(res) 
array([[1., 1.], [1., 1.]], dtype = float32)

batch_dot

It is used to perform the product of two data in batches. Input dimension must be 2 or higher. It is shown below −

>>> a_batch = k.ones(shape = (2,3)) 
>>> b_batch = k.ones(shape = (3,2)) 
>>> c_batch = k.batch_dot(a_batch,b_batch) 
>>> c_batch 
<tf.Tensor  ExpandDims:0  shape = (2, 1) dtype = float32>

variable

It is used to initiapzes a variable. Let us perform simple transpose operation in this variable.

>>> data = k.variable([[10,20,30,40],[50,60,70,80]]) 
#variable initiapzed here 
>>> result = k.transpose(data) 
>>> print(result) 
Tensor("transpose_6:0", shape = (4, 2), dtype = float32) 
>>> print(k.eval(result)) 
   [[10. 50.] 
   [20. 60.] 
   [30. 70.] 
   [40. 80.]]

If you want to access from numpy −

>>> data = np.array([[10,20,30,40],[50,60,70,80]]) 

>>> print(np.transpose(data)) 
   [[10 50] 
   [20 60] 
   [30 70] 
   [40 80]] 

>>> res = k.variable(value = data) 
>>> print(res) 
<tf.Variable  Variable_7:0  shape = (2, 4) dtype = float32_ref>

is_sparse(tensor)

It is used to check whether the tensor is sparse or not.

>>> a = k.placeholder((2, 2), sparse=True) 

>>> print(a) SparseTensor(indices =       
   Tensor("Placeholder_8:0", 
   shape = (?, 2), dtype = int64), 
values = Tensor("Placeholder_7:0", shape = (?,), 
dtype = float32), dense_shape = Tensor("Const:0", shape = (2,), dtype = int64)) 

>>> print(k.is_sparse(a)) True

to_dense()

It is used to converts sparse into dense.

>>> b = k.to_dense(a) 
>>> print(b) Tensor("SparseToDense:0", shape = (2, 2), dtype = float32) 
>>> print(k.is_sparse(b)) False

random_uniform_variable

It is used to initiapze using uniform distribution concept.

k.random_uniform_variable(shape, mean, scale)

Here,

shape − denotes the rows and columns in the format of tuples.

mean − mean of uniform distribution.

scale − standard deviation of uniform distribution.

Let us have a look at the below example usage −

>>> a = k.random_uniform_variable(shape = (2, 3), low=0, high = 1) 
>>> b = k. random_uniform_variable(shape = (3,2), low = 0, high = 1) 
>>> c = k.dot(a, b) 
>>> k.int_shape(c) 
(2, 2)

utils module

utils provides useful utipties function for deep learning. Some of the methods provided by the utils module is as follows −

HDF5Matrix

It is used to represent the input data in HDF5 format.

from keras.utils import HDF5Matrix data = HDF5Matrix( data.hdf5 ,  data )

to_categorical

It is used to convert class vector into binary class matrix.

>>> from keras.utils import to_categorical 
>>> labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 
>>> to_categorical(labels) 
array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.], 
   [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.], 
   [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.], 
   [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.], 
   [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.], 
   [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], 
   [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], 
   [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], 
   [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.], 
   [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]], dtype = float32)
>>> from keras.utils import normapze 
>>> normapze([1, 2, 3, 4, 5]) 
array([[0.13483997, 0.26967994, 0.40451992, 0.53935989, 0.67419986]])

print_summary

It is used to print the summary of the model.

from keras.utils import print_summary print_summary(model)

plot_model

It is used to create the model representation in dot format and save it to file.

from keras.utils import plot_model 
plot_model(model,to_file =  image.png )

This plot_model will generate an image to understand the performance of model.