imageClassificationExample.py

import tensorflow as tf

tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
print('Using TensorFlow version', tf.__version__)

# This is the dataset, load the data
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

print('x_train shape:', x_train.shape)
print('y_train shape:', y_train.shape)
print('x_test shape:', x_test.shape)
print('y_test shape:', y_test.shape)

from matplotlib import pyplot as plt

# Show an example data object
plt.imshow(x_train[0], cmap='binary')
plt.show()

print(y_train[0])
print(set(y_train))

from tensorflow.keras.utils import to_categorical

# Modify the outputs to be a 10-dimensional vector (containing categories)
y_train_encoded = to_categorical(y_train)
y_test_encoded = to_categorical(y_test)

print('y_train_encoded shape:', y_train_encoded.shape)
print('y_test_encoded shape:', y_test_encoded.shape)

print(y_train_encoded[0])

import numpy as np

x_train_reshaped = np.reshape(x_train, (60000, 784))
x_test_reshaped = np.reshape(x_test, (10000, 784))

print('x_train_reshaped shape:', x_train_reshaped.shape)
print('x_test_reshaped shape:', x_test_reshaped.shape)

print(set(x_train_reshaped[0]))

x_mean = np.mean(x_train_reshaped)
x_std = np.std(x_train_reshaped)

epsilon = 1e-10

x_train_norm = (x_train_reshaped - x_mean) / (x_std + epsilon)
x_test_norm = (x_test_reshaped - x_mean) / (x_std + epsilon)

print(set(x_train_norm[0]))

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(128, activation='relu', input_shape=(784,)),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

model.compile(
    optimizer='sgd',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

model.summary()

model.fit(x_train_norm, y_train_encoded, epochs=3)

loss, accuracy = model.evaluate(x_test_norm, y_test_encoded)
print('Test set accuracy:', accuracy * 100)

preds = model.predict(x_test_norm)
print('Shape of preds:', preds.shape)

plt.figure(figsize=(12, 12))

start_index = 0

for i in range(25):
    plt.subplot(5, 5, i + 1)
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])

    pred = np.argmax(preds[start_index + i])
    gt = y_test[start_index + i]

    col = 'g'
    if pred != gt:
        col = 'r'

    plt.xlabel('i={}, pred={}, gt={}'.format(start_index + i, pred, gt), color=col)
    plt.imshow(x_test[start_index + i], cmap='binary')
plt.show()

plt.plot(preds[8])
plt.show()