# pip3 install tensorflow


import tensorflow as tf
print("TensorFlow version:", tf.__version__)


# Load and prepare the MNIST dataset. The pixel values of the images range from 0 through 255.
# Scale these values to a range of 0 to 1 by dividing the values by 255.0.
# This also converts the sample data from integers to floating-point numbers:
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


# Build a tf.keras.Sequential model:
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)
])


# For each example, the model returns a vector of logits or log-odds scores, one for each class.
predictions = model(x_train[:1]).numpy()
predictions


# The tf.nn.softmax function converts these logits to probabilities for each class: 
tf.nn.softmax(predictions).numpy()