MNIST, FASION_MNIST, myCallback

MNIST_FASION_MNIST_myCallback.py

import tensorflow as tf
print(tf.__version__)

class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('acc')>=0.90):
      print("\nReached 90% accuracy so cancelling training!")
      self.model.stop_training = True

#mnist = tf.keras.datasets.fashion_mnist
mnist = tf.keras.datasets.mnist

(training_images, training_labels), (test_images, test_labels) = mnist.load_data()

'''
import matplotlib.pyplot as plt
plt.imshow(test_images[0])
plt.show()
plt.clf()

import numpy as np
np.set_printoptions(linewidth=200)
print(test_images[0])
'''

#training_images=training_images.reshape(60000, 28, 28, 1)
#test_images = test_images.reshape(10000, 28, 28, 1)
training_images=training_images/255.0
test_images=test_images/255.0

model = tf.keras.models.Sequential([
    #tf.keras.layers.Conv2D(64, (3, 3), activation=tf.nn.relu, input_shape=(28, 28, 1)),
    #tf.keras.layers.MaxPooling2D(2, 2),
    #tf.keras.layers.Conv2D(64, (3, 3), activation=tf.nn.relu),
    #tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Flatten(),
    #tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer=tf.optimizers.Adam(),
              loss=tf.losses.sparse_categorical_crossentropy,
              metrics=['acc'])

model.fit(training_images, training_labels, epochs=5, callbacks=[myCallback()])

model.evaluate(test_images, test_labels)

classifications = model.predict(test_images)

import matplotlib.pyplot as plt
#plt.plot(classifications[0])
#plt.axis([0, 9, 0, 1])
plt.bar([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], classifications[0])
plt.show()
plt.clf()
#plt.imshow(test_images[0])
#plt.show()
#plt.clf()
print(test_labels[0])