Dropout

# Bundled, deterministic handwritten-digits fixture.
from sklearn.datasets import load_digits as _course_load_digits

def _course_digits_load_data():
    course_digits = _course_load_digits()
    images = np.kron(
        course_digits.images.astype(np.float32),
        np.ones((3, 3), dtype=np.float32),
    )
    images = np.pad(images, ((0, 0), (2, 2), (2, 2))) * (255.0 / 16.0)
    labels = np.asarray(course_digits.target, dtype=np.int64)
    split = int(0.8 * len(images))
    return (images[:split], labels[:split]), (images[split:], labels[split:])

from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = _course_digits_load_data()

x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255

x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)

def train(d):
    print(f"Training with dropout = {d}")
    model = keras.Sequential([
        keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=(28,28,1)),
        keras.layers.MaxPooling2D(pool_size=(2, 2)),
        keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        keras.layers.MaxPooling2D(pool_size=(2, 2)),
        keras.layers.Flatten(),
        keras.layers.Dropout(d),
        keras.layers.Dense(10, activation="softmax")
    ])
    model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['acc'])
    hist = model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=2,batch_size=64)
    return hist

res = { d : train(d) for d in [0,0.2,0.5,0.8] }

for d,h in res.items():
    plt.plot(h.history['val_acc'],label=str(d))
plt.legend()