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-# pip3 install tensorflow
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-import tensorflow as tf
-print("TensorFlow version:", tf.__version__)
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-# 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
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-(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())
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-
-
-# 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
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-# 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)
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-# Configure and compile the model
-model.compile(optimizer='adam',
-              loss=loss_fn,
-              metrics=['accuracy'])
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-
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-# Use the Model.fit method to adjust your model parameters and minimize the loss: 
-model.fit(x_train, y_train, epochs=5)
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-# 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)
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-# If you want your model to return a probability, you can wrap the trained model, and attach the softmax to it:
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-probability_model = tf.keras.Sequential([
-  model,
-  tf.keras.layers.Softmax()
-])
-probability_model(x_test[:5])
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