basic_classification.ipynb

TensorFlow and tf.keras

import tensorflow as tf from tensorflow import keras

Helper libraries

import numpy as np import matplotlib.pyplot as plt

print(tf.version)

fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

print(train_images.shape) print(len(train_labels)) print(train_labels) print(test_images.shape)

plt.figure() plt.imshow(train_images[0]) plt.colorbar() plt.grid(False) plt.show()

train_images = train_images / 255.0 test_images = test_images / 255.0

plt.figure(figsize=(10,10)) for i in range(25): plt.subplot(5,5,i+1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(train_images[i], cmap=plt.cm.binary) plt.xlabel(class_names[train_labels[i]]) plt.show()

model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10, activation='softmax') ])

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

model.fit(train_images, train_labels, epochs=10)

test_loss, test_acc = model.evaluate(test_images, test_labels) print('\nTest accuracy:', test_acc)

predictions = model.predict(test_images) predictions[0] np.argmax(predictions[0]) test_labels[0]

def plot_image(i, predictions_array, true_label, img): predictions_array, true_label, img = predictions_array[i], true_label[i], img[i] plt.grid(False) plt.xticks([]) plt.yticks([])

plt.imshow(img, 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 labels, and the true labels.

Color correct predictions in blue and incorrect predictions in red.

num_rows = 5 num_cols = 3 num_images = num_rowsnum_cols plt.figure(figsize=(22num_cols, 2num_rows)) for i in range(num_images): plt.subplot(num_rows, 2num_cols, 2i+1) plot_image(i, predictions, test_labels, test_images) plt.subplot(num_rows, 2num_cols, 2i+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.expand_dims(img,0)) print(img.shape)

predictions_single = model.predict(img) print(predictions_single)

plot_value_array(0, predictions_single, testlabels) = plt.xticks(range(10), class_names, rotation=45)

np.argmax(predictions_single[0])

simplysameer333 / MachineLearning

fashion_mnist.py #16