Graph out training results

flow.py

@@ -8,7 +8,7 @@ import matplotlib.pyplot as plt
 
 import random
 
-print(tf.__version__)
+print("Running TensorFlow", tf.__version__)
 
 fashion_mnist = keras.datasets.fashion_mnist
 
@@ -33,7 +33,7 @@ class_names = [
 model = keras.Sequential(
     [
         keras.layers.Flatten(input_shape=(28, 28)),
-        keras.layers.Dense(128, activation=tf.nn.relu),
+        keras.layers.Dense(256, activation=tf.nn.relu),
         keras.layers.Dense(10, activation=tf.nn.softmax),
     ]
 )
@@ -44,11 +44,35 @@ model.compile(
     metrics=["accuracy"],
 )
 
-model.fit(train_images, train_labels, epochs=5)
 
-test_loss, test_acc = model.evaluate(test_images, test_labels)
+def plot_training(history):
+    acc = history.history["acc"]
+    val_acc = history.history["val_acc"]
 
-print("Test accuracy:", test_acc)
+    epochs = range(1, len(acc) + 1)
+
+    plt.plot(epochs, acc, "bo", label="Training acc")
+    plt.plot(epochs, val_acc, "b", label="Validation acc")
+    plt.title("Training and validation accuracy")
+    plt.xlabel("Epochs")
+    plt.ylabel("Accuracy")
+    plt.legend()
+
+    plt.show()
+
+
+early_stop = keras.callbacks.EarlyStopping(monitor="val_loss", patience=5)
+
+history = model.fit(
+    train_images,
+    train_labels,
+    epochs=64,
+    batch_size=512,
+    validation_data=(test_images, test_labels),
+    callbacks=[early_stop],
+)
+
+plot_training(history)
 
 predictions = model.predict(test_images)