test.py

# Causal Dynamical Triangulations in C++ using CGAL
#
# Copyright © 2021 Adam Getchell
#
# First pass at ML for CDT++
 
# @file test.py
# @brief ML using TensorFlow
# @author Adam Getchell
 
# Usage: python test.py
#
 
import tensorflow as tf
 
mnist = tf.keras.datasets.mnist
 
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
 
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
])
 
predictions = model(x_train[:1]).numpy()
 
tf.nn.softmax(predictions).numpy()
 
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 
loss_fn(y_train[:1], predictions).numpy()
 
model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])
 
model.fit(x_train, y_train, epochs=5)
 
model.evaluate(x_test, y_train, verbose=2)
 
probability_model = tf.keras.Sequential([
    model,
    tf.keras.layers.Softmax()
])
 
probability_model(x_test[:5])