# 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


# You can preview the raw data prior to training the model
print(mnist.load_data())





# 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()


# Define a loss function for training using losses.SparseCategoricalCrossentropy:
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)


# Configure and compile the model
model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])






# Use the Model.fit method to adjust your model parameters and minimize the loss: 
model.fit(x_train, y_train, epochs=5)


# The Model.evaluate method checks the model's performance, usually on a validation set or test set.
model.evaluate(x_test,  y_test, verbose=2)


# If you want your model to return a probability, you can wrap the trained model, and attach the softmax to it:

probability_model = tf.keras.Sequential([
  model,
  tf.keras.layers.Softmax()
])
probability_model(x_test[:5])