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+# 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])
+
+
+