Create app.py

app.py

@@ -0,0 +1,31 @@
+import tensorflow as tf
+print("TensorFlow version:", tf.__version__)
+
+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()
+predictions
+
+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_test, verbose=2)