simple fashion_mnist classification with a dnn

simple-dnn.py

import tensorflow as tf
print(tf.__version__)

class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('loss')<0.25):
      print("\nReached 75% accuracy so cancelling training!")
      self.model.stop_training = True

callbacks = myCallback()

mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()


training_images=training_images/255.0
test_images=test_images/255.0

#The first layer in your network should be the same shape as your data. 
#Right now our data is 28x28 images, and 28 layers of 28 neurons would be infeasible, 
#so it makes more sense to 'flatten' that 28,28 into a 784x1. 
#Instead of wriitng all the code to handle that ourselves, we add the Flatten() layer at the begining,
#and when the arrays are loaded into the model later, they'll automatically be flattened for us. 

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28,28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.fit(training_images, training_labels, epochs=10, callbacks=[callbacks])
model.evaluate(test_images, test_labels)
classifications = model.predict(test_images)
print(classifications[0])
print(test_labels[0])