Introduction to TensorFlow W4:Using Real-world Images

by Laurence Moroney

1 download data

1.1 download horse-or-human.zip

Code

import urllib.request
urllib.request.urlretrieve("https://storage.googleapis.com/tensorflow-1-public/course2/week3/horse-or-human.zip", "horse-or-human.zip")

1.2 download validation-horse-or-human.zip

Code

import urllib.request
urllib.request.urlretrieve("https://storage.googleapis.com/tensorflow-1-public/course2/week3/validation-horse-or-human.zip", "validation-horse-or-human.zip")

Code

import zipfile
#| eval: false
# Unzip training set
local_zip = './horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('./horse-or-human')

# Unzip validation set
local_zip = './validation-horse-or-human.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('./validation-horse-or-human')

zip_ref.close()

Code

from PIL import Image

im = Image.open('./horse-or-human/horses/horse01-0.png')
im.size # (width,height)

(300, 300)

Code

import os

# Directory with training horse pictures
train_horse_dir = os.path.join('./horse-or-human/horses')

# Directory with training human pictures
train_human_dir = os.path.join('./horse-or-human/humans')

# Directory with validation horse pictures
validation_horse_dir = os.path.join('./validation-horse-or-human/horses')

# Directory with validation human pictures
validation_human_dir = os.path.join('./validation-horse-or-human/humans')

Code

train_horse_names = os.listdir(train_horse_dir)
print(f'TRAIN SET HORSES: {train_horse_names[:10]}')

train_human_names = os.listdir(train_human_dir)
print(f'TRAIN SET HUMANS: {train_human_names[:10]}')

validation_horse_names = os.listdir(validation_horse_dir)
print(f'VAL SET HORSES: {validation_horse_names[:10]}')

validation_human_names = os.listdir(validation_human_dir)
print(f'VAL SET HUMANS: {validation_human_names[:10]}')

TRAIN SET HORSES: ['horse43-5.png', 'horse06-5.png', 'horse20-6.png', 'horse04-7.png', 'horse41-7.png', 'horse22-4.png', 'horse19-2.png', 'horse24-2.png', 'horse37-8.png', 'horse02-1.png']
TRAIN SET HUMANS: ['human17-22.png', 'human10-17.png', 'human10-03.png', 'human07-27.png', 'human09-22.png', 'human05-22.png', 'human02-03.png', 'human02-17.png', 'human15-27.png', 'human12-12.png']
VAL SET HORSES: ['horse1-204.png', 'horse2-112.png', 'horse3-498.png', 'horse5-032.png', 'horse5-018.png', 'horse1-170.png', 'horse5-192.png', 'horse1-411.png', 'horse4-232.png', 'horse3-070.png']
VAL SET HUMANS: ['valhuman04-20.png', 'valhuman03-01.png', 'valhuman04-08.png', 'valhuman03-15.png', 'valhuman01-04.png', 'valhuman01-10.png', 'valhuman01-11.png', 'valhuman01-05.png', 'valhuman03-14.png', 'valhuman03-00.png']

Code

print(f'total training horse images: {len(os.listdir(train_horse_dir))}')
print(f'total training human images: {len(os.listdir(train_human_dir))}')
print(f'total validation horse images: {len(os.listdir(validation_horse_dir))}')
print(f'total validation human images: {len(os.listdir(validation_human_dir))}')

total training horse images: 500
total training human images: 527
total validation horse images: 128
total validation human images: 128

Code

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

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

# Index for iterating over images
pic_index = 0

# Set up matplotlib fig, and size it to fit 4x4 pics
fig = plt.gcf()
fig.set_size_inches(ncols * 4, nrows * 4)

pic_index += 8
next_horse_pix = [os.path.join(train_horse_dir, fname) 
                for fname in train_horse_names[pic_index-8:pic_index]]
next_human_pix = [os.path.join(train_human_dir, fname) 
                for fname in train_human_names[pic_index-8:pic_index]]

for i, img_path in enumerate(next_horse_pix+next_human_pix):
  # Set up subplot; subplot indices start at 1
  sp = plt.subplot(nrows, ncols, i + 1)
  sp.axis('Off') # Don't show axes (or gridlines)

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

plt.show()

Code

import tensorflow as tf
import numpy as np
from tensorflow import keras
import os
print(tf.__version__)

2.16.1

2 Load the data

picture file structure

ImageDataGenerator: All images will be resized to 300x300

Code

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1/255)
validation_datagen = ImageDataGenerator(rescale=1/255)

# Flow training images in batches of 128 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
        './horse-or-human/',  # This is the source directory for training images
        target_size=(300, 300),  # All images will be resized to 300x300
        batch_size=128,
        # Since you use binary_crossentropy loss, you need binary labels
        class_mode='binary')

# Flow validation images in batches of 128 using validation_datagen generator
validation_generator = validation_datagen.flow_from_directory(
        './validation-horse-or-human/',  # This is the source directory for validation images
        target_size=(300, 300),  # All images will be resized to 300x300
        batch_size=32,
        # Since you use binary_crossentropy loss, you need binary labels
        class_mode='binary')

Found 1027 images belonging to 2 classes.
Found 256 images belonging to 2 classes.

3 define convolutional model

input 300 by 300 color image

flow into 16 3by3 convolutional layers and 2by2 pooling

output is 1 neural(0/1) sigmoid function.its good for binary

Code

import tensorflow as tf

model = tf.keras.models.Sequential([
    # Note the input shape is the desired size of the image 300x300 with 3 bytes color
    # This is the first convolution
    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 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')
])

Code

# Print the model summary
model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv2d (Conv2D)                 │ (None, 298, 298, 16)   │           448 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d (MaxPooling2D)    │ (None, 149, 149, 16)   │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_1 (Conv2D)               │ (None, 147, 147, 32)   │         4,640 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_1 (MaxPooling2D)  │ (None, 73, 73, 32)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_2 (Conv2D)               │ (None, 71, 71, 64)     │        18,496 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_2 (MaxPooling2D)  │ (None, 35, 35, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_3 (Conv2D)               │ (None, 33, 33, 64)     │        36,928 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_3 (MaxPooling2D)  │ (None, 16, 16, 64)     │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv2d_4 (Conv2D)               │ (None, 14, 14, 64)     │        36,928 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ max_pooling2d_4 (MaxPooling2D)  │ (None, 7, 7, 64)       │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ flatten (Flatten)               │ (None, 3136)           │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 512)            │     1,606,144 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 1)              │           513 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 1,704,097 (6.50 MB)

 Trainable params: 1,704,097 (6.50 MB)

 Non-trainable params: 0 (0.00 B)

4 compile model

Code

# v2.11+ optimizer `tf.keras.optimizers.RMSprop` runs slowly on M1/M2 Macs
from tensorflow.keras.optimizers import RMSprop

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

5 Callbacks

Code

class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    '''
    Halts the training when the loss falls below 0.15

    Args:
      epoch (integer) - index of epoch (required but unused in the function definition below)
      logs (dict) - metric results from the training epoch
    '''

    # Check the loss
    if(logs.get('loss') < 0.15):

      # Stop if threshold is met
      print("\nLoss is lower than 0.2 so cancelling training!")
      print("cancelling training with:")
      print(epoch+1)
      self.model.stop_training = True

# Instantiate class
callbacks = myCallback()

6 train model

Code

history = model.fit(
      train_generator,
      steps_per_epoch=8,  
      epochs=10,
      verbose=0,
      validation_data = validation_generator,
      validation_steps=8,
      callbacks=[callbacks])


Loss is lower than 0.2 so cancelling training!
cancelling training with:
10

7 Prediction

Prediction a validation horses

Code

import numpy as np

from tensorflow.keras.utils import load_img, img_to_array

fn='horse1-241.png'
path = './validation-horse-or-human/horses/'+ fn
img = load_img(path, target_size=(300, 300))
x = img_to_array(img)
x /= 255
x = np.expand_dims(x, axis=0)

images = np.vstack([x])
classes = model.predict(images, batch_size=10)
print(classes[0])
if classes[0]>0.5:
  print(fn+" is a human")
else:
  print(fn+" is a horse")

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 44ms/step1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 45ms/step
[5.7587976e-11]
horse1-241.png is a horse

Prediction a human from internet

Code

import numpy as np

from tensorflow.keras.utils import load_img, img_to_array

fn='daniel craig.png'
path = './random/'+ fn
img = load_img(path, target_size=(300, 300))
x = img_to_array(img)
x /= 255
x = np.expand_dims(x, axis=0)

images = np.vstack([x])
classes = model.predict(images, batch_size=10)
print(classes[0])
if classes[0]>0.5:
  print(fn+" is a human")
else:
  print(fn+" is a horse")

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step
[0.01623141]
daniel craig.png is a horse

8 resource:

https://www.coursera.org/learn/introduction-tensorflow/home/info

https://github.com/https-deeplearning-ai/tensorflow-1-public/tree/main/C1

https://github.com/zalandoresearch/fashion-mnist