best_mnist_keras.py

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

import numpy as np

from tensorflow import keras
from tensorflow.keras import layers

inputs = keras.Input(shape=(784,), name='digits')
x = layers.Dense(64, activation='relu', name='dense_1')(inputs)
x = layers.Dense(64, activation='relu', name='dense_2')(x)
outputs = layers.Dense(10, name='predictions')(x)

model = keras.Model(inputs=inputs, outputs=outputs)

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

# Preprocess the data (these are Numpy arrays)
x_train = x_train.reshape(60000, 784).astype('float32') / 255
x_test = x_test.reshape(10000, 784).astype('float32') / 255

y_train = y_train.astype('float32')
y_test = y_test.astype('float32')

# Reserve 10,000 samples for validation
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]

model.compile(optimizer=keras.optimizers.RMSprop(),  # Optimizer
              # Loss function to minimize
              loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              # List of metrics to monitor
              metrics=['sparse_categorical_accuracy'])

print('# Fit model on training data')
history = model.fit(x_train, y_train,
                    batch_size=64,
                    epochs=3,
                    # We pass some validation for
                    # monitoring validation loss and metrics
                    # at the end of each epoch
                    validation_data=(x_val, y_val))

print('\nhistory dict:', history.history)

# Evaluate the model on the test data using `evaluate`
print('\n# Evaluate on test data')
results = model.evaluate(x_test, y_test, batch_size=128)
print('test loss, test acc:', results)

print('\n# Generate predictions for 3 samples')
k = np.array(x_train[4545])
print(k.shape)
k = k.reshape(1, 784)
predictions = model.predict(
    k)  # this will return a list of 10 elements pick the one with the heights probability
print(predictions)
print('predictions shape:', predictions.shape)

print(np.argmax(predictions))  # will return the index of the max probability