utf-8''Exercise4-Question.py

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

# Below is code with a link to a happy or sad dataset which contains 80 images, 40 happy and 40 sad. 
# Create a convolutional neural network that trains to 100% accuracy on these images,  which cancels training upon hitting training accuracy of >.999
# 
# Hint -- it will work best with 3 convolutional layers.

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

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

zip_ref = zipfile.ZipFile(path, 'r')
zip_ref.extractall("/tmp/h-or-s")
zip_ref.close()


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

    DESIRED_ACCURACY = 0.999

    class myCallback(tf.keras.callbacks.Callback):
         # Your Code
        def on_epoch_end(self, epoch, logs={}):
            if(logs.get('acc')>0.999):
                print("\nReached 99.9% accuracy so cancelling training!")
                self.model.stop_training = True
    
    # This Code Block should Define and Compile the Model. Please assume the images are 150 X 150 in your implementation.
    model = tf.keras.models.Sequential([
        # Your Code Here
          tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),
          tf.keras.layers.MaxPooling2D(2, 2),
    # The second convolution
          tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
          tf.keras.layers.MaxPooling2D(2,2),
    # The third convolution
          tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
          tf.keras.layers.MaxPooling2D(2,2),
    # The fourth convolution
   #tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    #tf.keras.layers.MaxPooling2D(2,2),
    # The fifth convolution
    #tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    #tf.keras.layers.MaxPooling2D(2,2),
    # Flatten the results to feed into a DNN
          tf.keras.layers.Flatten(),
    # 512 neuron hidden layer
          tf.keras.layers.Dense(512, activation='relu'),
    # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans')
          tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    callbacks=myCallback()
    from tensorflow.keras.optimizers import RMSprop

    model.compile(loss='binary_crossentropy',
              optimizer=RMSprop(lr=0.001),
              metrics=['accuracy'])
        

    # This code block should create an instance of an ImageDataGenerator called train_datagen 
    # And a train_generator by calling train_datagen.flow_from_directory

    from tensorflow.keras.preprocessing.image import ImageDataGenerator

    train_datagen = ImageDataGenerator(rescale=1/255)

    # Please use a target_size of 150 X 150.
    train_generator = train_datagen.flow_from_directory("/tmp/h-or-s",  # This is the source directory for training images
        target_size=(150, 150),  
        batch_size=128,
        class_mode='binary')
    # Expected output: 'Found 80 images belonging to 2 classes'

    # This code block should call model.fit_generator and train for
    # a number of epochs.
    # model fitting
    history = model.fit_generator(
           train_generator,
      steps_per_epoch=8,  
      epochs=15,
      verbose=1,
      callbacks=[callbacks])
    # model fitting
    return history.history['acc'][-1]


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# The Expected output: "Reached 99.9% accuracy so cancelling training!""
train_happy_sad_model()


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# Now click the 'Submit Assignment' button above.
# Once that is complete, please run the following two cells to save your work and close the notebook


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get_ipython().run_cell_magic('javascript', '', '<!-- Save the notebook -->\nIPython.notebook.save_checkpoint();')


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get_ipython().run_cell_magic('javascript', '', 'IPython.notebook.session.delete();\nwindow.onbeforeunload = null\nsetTimeout(function() { window.close(); }, 1000);')


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