[INTRODUCTION TO TENSORFLOW FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING] Improving DNN Performance using Convolutions

코세라의   deeplearning.AI tensorflow developer 전문가 자격증 과정내에

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

과정의 3주차 Enhancing Vision with Convolutional Neural Networks

챕터의 코드 예제입니다.

 

Improving DNN Performance using Convolutions

1) Convolution 2D layer, Max pooling layer를 2개 층을 쌓는다.

2) 마지막에 dense layer를 추가한다.

 

Dense layer만 쓴 것 보다, 정확도가 더 좋아진다.

#!/usr/bin/env python
# coding: utf-8

# ## Exercise 3
# In the videos you looked at how you would improve Fashion MNIST using Convolutions. For your exercise see if you can improve MNIST to 99.8% accuracy or more using only a single convolutional layer and a single MaxPooling 2D. You should stop training once the accuracy goes above this amount. It should happen in less than 20 epochs, so it's ok to hard code the number of epochs for training, but your training must end once it hits the above metric. If it doesn't, then you'll need to redesign your layers.
# 
# I've started the code for you -- you need to finish it!
# 
# When 99.8% accuracy has been hit, you should print out the string "Reached 99.8% accuracy so cancelling training!"
# 

# In[20]:


import tensorflow as tf
from os import path, getcwd, chdir

# DO NOT CHANGE THE LINE BELOW. If you are developing in a local
# environment, then grab mnist.npz from the Coursera Jupyter Notebook
# and place it inside a local folder and edit the path to that location
path = f"{getcwd()}/../tmp2/mnist.npz"


# In[24]:


config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)


# In[27]:


# GRADED FUNCTION: train_mnist_conv
def train_mnist_conv():
    # Please write your code only where you are indicated.
    # please do not remove model fitting inline comments.

    # YOUR CODE STARTS HERE
    class myCallback(tf.keras.callbacks.Callback):
        def on_epoch_end(self, epoch, logs={}):
            if(logs.get('acc')>0.998):
              print("Reached 99.8% accuracy so cancelling training!")
              self.model.stop_training = True
    # YOUR CODE ENDS HERE

    mnist = tf.keras.datasets.mnist
    (training_images, training_labels), (test_images, test_labels) = mnist.load_data(path=path)
    # YOUR CODE STARTS HERE
    training_images=training_images.reshape(60000, 28, 28, 1)
    training_images=training_images / 255.0
    test_images = test_images.reshape(10000, 28, 28, 1)
    test_images=test_images/255.0
    
    callbacks = myCallback()
    # YOUR CODE ENDS HERE

    model = tf.keras.models.Sequential([
            # YOUR CODE STARTS HERE
        tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2,2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(10, activation='softmax')
            # YOUR CODE ENDS HERE
    ])

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    # model fitting
    history = model.fit(
        # YOUR CODE STARTS HERE
    training_images, training_labels, epochs=20, callbacks=[callbacks]
        # YOUR CODE ENDS HERE
    )
    # model fitting
    return history.epoch, history.history['acc'][-1]


# In[28]:


_, _ = train_mnist_conv()