mnist/training/simple.py

# -*- coding: utf-8 -*-
"""simple.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1CGpActfOTQuiUkle2q40rg8Bf7ijfyAU
"""

from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.utils import to_categorical

(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Reshaping for channels_last (tensorflow) with one channel
size = 28
print(x_train.shape, x_test.shape)
x_train = x_train.reshape(len(x_train), size, size, 1).astype('float32')
x_test = x_test.reshape(len(x_test), size, size, 1).astype('float32')
print(x_train.shape, x_test.shape)

# Normalize
upper = max(x_train.max(), x_test.max())
lower = min(x_train.min(), x_test.min())
print(f'Max: {upper} Min: {lower}')
x_train /= upper
x_test /= upper

total_classes = 10
y_train = to_categorical(y_train, total_classes)
y_test = to_categorical(y_test, total_classes)

# Make the model
model = Sequential()
model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(size,size, 1), data_format='channels_last'))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(total_classes, activation='softmax'))

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

# Train
model.fit(x_train, y_train,
          batch_size=32,
          epochs=12,
          verbose=True)

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# Save for keras
model.save("model.h5")

!pip install tensorflowjs
import tensorflowjs as tfjs

# Save for the web
tfjs.converters.save_keras_model(model, './js')