fashion_mnist_with_kerastuner.py

# -*- coding: utf-8 -*-
"""cnn_2.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1whV1dT9lnjhLFtyk0wqzHYlsmIpO3F65
"""

!pip install keras-tuner # its for google colab

import tensorflow as tf
import keras
import numpy as np

fashion= keras.datasets.fashion_mnist

(train_images, train_lebels), (test_images, test_lebels)= fashion.load_data()

# first of all we need to normalize the images. So that all pixel values range between 0 to 1
train_images= train_images/255.0
test_images= test_images/255.0

train_images[0]

train_images[0].shape

# then we need to reshape the data or the images
train_images= train_images.reshape (len(train_images), 28,28,1)
test_images= test_images.reshape (len(test_images), 28,28,1)

def build_model (hp):
  model= keras.Sequential([
         keras.layers.Conv2D(filters=hp.Int ('conv_1_filter', min_value=32, max_value=128, step=16), 
         kernel_size=hp.Choice ('conv_1_kernel',values=[3,5]),
         activation='relu', input_shape=(28,28,1)
         ),
         keras.layers.Conv2D (filters=hp.Int ('conv_2_filter', min_value=32, max_value=128, step=16),
         kernel_size= hp.Choice ('conv_2_kernel', values=[3,5]),
         activation='relu'),
         keras.layers.Flatten () ,
         keras.layers.Dense (units= hp.Int ('dense_1_units', min_value=32, max_value=128, step=16),
         activation='relu'),
         keras.layers.Dense (10, activation='softmax')                 
  ])
  model.compile (optimizer=keras.optimizers.Adam (hp.Choice ('learning_rate', values=[1e-2, 1e-3])), loss='sparse_categorical_crossentropy', 
                 metrics=['accuracy'])
  return model

from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters

tuner_search= RandomSearch (build_model, objective='val_accuracy', max_trials=5, directory='output', project_name='mnist_fashion')

tuner_search.search (train_images, train_lebels, epochs=3, validation_split=0.1)

# getting best model
model= tuner_search.get_best_models (num_models=1)[0]

model.summary ()

model.fit (train_images, train_lebels, epochs=10, validation_split=0.1, initial_epoch=3)

model.evaluate(test_images, test_lebels)

test_lebels[0]