C1W3: Improve MNIST with Convolutions#

import tensorflow as tf
import numpy as np

Load the data#

MNIST dataset

  • 60,000 28x28 grayscale images of the 10 digits

tf.keras.datasets.mnist.load_data

(training_images, training_labels), _ = tf.keras.datasets.mnist.load_data()

Pre-processing the data#

def reshape_and_normalize(images):
    
    images = np.expand_dims(images, -1)
     
    images = images / 255

    return images

training_images = reshape_and_normalize(training_images)

print(f"Maximum pixel value after normalization: {np.max(training_images)}\n")
print(f"Shape of training set after reshaping: {training_images.shape}\n")
print(f"Shape of one image after reshaping: {training_images[0].shape}")

Maximum pixel value after normalization: 1.0

Shape of training set after reshaping: (60000, 28, 28, 1)

Shape of one image after reshaping: (28, 28, 1)

Defining your callback#

class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if logs.get('accuracy') is not None and logs.get('accuracy') >= 0.995:
            print("\nReached 99.5 accuracy so canceling training")
            self.model.stop_training = True

Convolutional Model#

def convolutional_model():

    model = tf.keras.models.Sequential([ 
        tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),
        tf.keras.layers.MaxPooling2D(),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(10, activation='softmax')
    ]) 

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

model = convolutional_model()

callbacks = myCallback()

history = model.fit(training_images, training_labels, epochs=10, callbacks=[callbacks])

Epoch 1/10
1875/1875 [==============================] - 15s 6ms/step - loss: 0.1539 - accuracy: 0.9538
Epoch 2/10
1875/1875 [==============================] - 12s 6ms/step - loss: 0.0527 - accuracy: 0.9840
Epoch 3/10
1875/1875 [==============================] - 12s 6ms/step - loss: 0.0322 - accuracy: 0.9901
Epoch 4/10
1875/1875 [==============================] - 11s 6ms/step - loss: 0.0210 - accuracy: 0.9934
Epoch 5/10
1874/1875 [============================>.] - ETA: 0s - loss: 0.0146 - accuracy: 0.9954
Reached 99.5 accuracy so canceling training
1875/1875 [==============================] - 12s 6ms/step - loss: 0.0146 - accuracy: 0.9954

print(f"Your model was trained for {len(history.epoch)} epochs")

Your model was trained for 5 epochs