imagerecognize_cnn.py

from __future__ import absolute_import, division, print_function


# Import TensorFlow and TensorFlow Datasets
import tensorflow as tf
import tensorflow_datasets as tfds
tf.logging.set_verbosity(tf.logging.ERROR)

# Helper libraries
import math
import numpy as np
import matplotlib.pyplot as plt

# Improve progress bar display
import tqdm
import tqdm.auto
tqdm.tqdm = tqdm.auto.tqdm


print(tf.__version__)

# This will go away in the future.
# If this gives an error, you might be running TensorFlow 2 or above
# If so, the just comment out this line and run this cell again
tf.enable_eager_execution()

dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)
train_dataset, test_dataset = dataset['train'], dataset['test']

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 
'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
num_train_examples = metadata.splits['train'].num_examples
num_test_examples = metadata.splits['test'].num_examples
print("Number of training examples: {}".format(num_train_examples))
print("Number of test examples: {}".format(num_test_examples))

def normalize(images, labels):
	images = tf.cast(images, tf.float32)
	images /= 255
	return images, labels

# The map function applies the normalize function to each element in the train
# and test datasets
train_dataset = train_dataset.map(normalize)
test_dataset = test_dataset.map(normalize)

# Take a single image, and remove the color dimension by reshaping
for image, label in test_dataset.take(1):
	break
image = image.numpy().reshape((28,28))

# Plot the image - voila a piece of fashion clothing
plt.figure()
plt.imshow(image, cmap=plt.cm.binary)
plt.colorbar()
plt.grid(False)
plt.show()

plt.figure(figsize=(10,10))
i=0
for (image, label) in test_dataset.take(25):
	image = image.numpy().reshape((28,28))
	plt.subplot(5,5,i+1)
	plt.xticks([])
	plt.yticks([])
	plt.grid(False)
	plt.imshow(image, cmap=plt.cm.binary)
	plt.xlabel(class_names[label])
	i += 1
plt.show()

model = tf.keras.Sequential([
	tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu, 
		input_shape=(28, 28,1)),
	tf.keras.layers.MaxPooling2D((2,2), strides=2),
	tf.keras.layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu),
	tf.keras.layers.MaxPooling2D((2,2), strides=2),
	tf.keras.layers.Flatten(),
	tf.keras.layers.Dense(128, activation=tf.nn.relu),
	tf.keras.layers.Dense(10, activation=tf.nn.softmax)
	])

model.compile(optimizer='adam',
	loss='sparse_categorical_crossentropy',
	metrics=['accuracy'])

BATCH_SIZE = 32
train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)

model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))

test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))
print('Accuracy on test dataset:', test_accuracy)

for test_images, test_labels in test_dataset.take(1):
	test_images = test_images.numpy()
	test_labels = test_labels.numpy()
	predictions = model.predict(test_images)

print(predictions.shape)
print(predictions[0])
print(np.argmax(predictions[0]))

def plot_image(i, predictions_array, true_labels, images):
	predictions_array, true_label, img = predictions_array[i], true_labels[i], images[i]
	plt.grid(False)
	plt.xticks([])
	plt.yticks([])
	
	plt.imshow(img[...,0], cmap=plt.cm.binary)

	predicted_label = np.argmax(predictions_array)
	if predicted_label == true_label:
		color = 'blue'
	else:
		color = 'red'
	
	plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
		100*np.max(predictions_array),
		class_names[true_label]),
		color=color)

def plot_value_array(i, predictions_array, true_label):
	predictions_array, true_label = predictions_array[i], true_label[i]
	plt.grid(False)
	plt.xticks([])
	plt.yticks([])
	thisplot = plt.bar(range(10), predictions_array, color="#777777")
	plt.ylim([0, 1]) 
	predicted_label = np.argmax(predictions_array)

	thisplot[predicted_label].set_color('red')
	thisplot[true_label].set_color('blue')

i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)
plt.show()

i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)
plt.show()

# Plot the first X test images, their predicted label, and the true label
# Color correct predictions in blue, incorrect predictions in red
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
	plt.subplot(num_rows, 2*num_cols, 2*i+1)
	plot_image(i, predictions, test_labels, test_images)
	plt.subplot(num_rows, 2*num_cols, 2*i+2)
	plot_value_array(i, predictions, test_labels)
plt.show()

# Grab an image from the test dataset
img = test_images[0]

print(img.shape)

# Add the image to a batch where it's the only member.
img = np.array([img])

print(img.shape)

predictions_single = model.predict(img)

print(predictions_single)

plot_value_array(0, predictions_single, test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)
plt.show()

print(np.argmax(predictions_single[0]))