Course_2_Week_1_Project_1.py

"""
This is is a part of the DeepLearning.AI TensorFlow Developer Professional Certificate offered on Coursera.

All copyrights belong to them. I am sharing this work here to showcase the projects I have worked on
Course: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Week 1: Exploring a Larger Dataset

Aim: Binary classification using a larger dataset
"""

import os
import zipfile
from os import path, getcwd, chdir
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator

local_zip = f"{getcwd()}/Dataset/cats_and_dogs_filtered.zip"
zip_ref=zipfile.ZipFile(local_zip, "r")
zip_ref.extractall("/Dataset")
zip_ref.close()

base_dir = "/Dataset/cats_and_dogs_filtered"

train_dir = os.path.join(base_dir, "train")
validation_dir = os.path.join(base_dir, "validation")

train_cats_dir = os.path.join(train_dir, "cats")
train_dogs_dir = os.path.join(train_dir, "dogs")

validation_cats_dir = os.path.join(validation_dir, "cats")
validation_dogs_dir = os.path.join(validation_dir, "dogs")

train_cats_fnames = os.listdir(train_cats_dir)
train_dogs_fnames = os.listdir(train_dogs_dir)

print(train_cats_fnames[:10])
print(train_dogs_fnames[:10])

print("Total training cat images: ", len(os.listdir(train_cats_dir)))
print("Total training dog images: ", len(os.listdir(train_dogs_dir)))
print("Total Validation cat images: ", len(os.listdir(validation_cats_dir)))
print("Total Validation dog images: ", len(os.listdir(validation_dogs_dir)))

# Parameters for our graph; we'll output images in a 4x4 configuration
nrows = 4
ncols = 4

pic_index = 0 # Index for iterating over images

fig = plt.gcf()
fig.set_size_inches(ncols*4, nrows*4)

pic_index+=8

next_cat_pix = [os.path.join(train_cats_dir, fname)
                for fname in train_cats_fnames[ pic_index-8:pic_index]
               ]

next_dog_pix = [os.path.join(train_dogs_dir, fname)
                for fname in train_dogs_fnames[ pic_index-8:pic_index]
               ]

for i, img_path in enumerate(next_cat_pix+next_dog_pix):
  # Set up subplot; subplot indices start at 1
  sp = plt.subplot(nrows, ncols, i + 1)
  sp.axis('Off')

  img = mpimg.imread(img_path)
  plt.imshow(img)

plt.show()

model = tf.keras.models.Sequential([
    # Note the input shape is the desired size of the image 150x150 with 3 bytes color
    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    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 ('cats') and 1 for the other ('dogs')
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.summary()

model.compile(optimizer=keras.optimizers.RMSprop(lr=0.001),
              loss= "binary_crossentropy",
              metrics=["acc"])

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale=1/255)
validation_datagen = ImageDataGenerator(rescale=1/255)

train_generator = train_datagen.flow_from_directory(train_dir, batch_size=20, class_mode="binary", target_size=(150,150))
validation_generator = train_datagen.flow_from_directory(validation_dir, batch_size=20, class_mode="binary", target_size=(150,150))

history = model.fit_generator(train_generator, validation_data=validation_generator, steps_per_epoch=100, epochs=15, validation_steps=50, verbose=2)

#-----------------------------------------------------------
# Retrieve a list of list results on training and test data
# sets for each training epoch
#-----------------------------------------------------------
acc      = history.history[     'accuracy' ]
val_acc  = history.history[ 'val_accuracy' ]
loss     = history.history[    'loss' ]
val_loss = history.history['val_loss' ]

epochs   = range(len(acc)) # Get number of epochs

#------------------------------------------------
# Plot training and validation accuracy per epoch
#------------------------------------------------
plt.plot  ( epochs,     acc )
plt.plot  ( epochs, val_acc )
plt.title ('Training and validation accuracy')
plt.figure()

#------------------------------------------------
# Plot training and validation loss per epoch
#------------------------------------------------
plt.plot  ( epochs,     loss )
plt.plot  ( epochs, val_loss )
plt.title ('Training and validation loss')