Simple MNIST convnet
Author: fchollet
Date created: 2015/06/19
Last modified: 2020/04/21
Description: A simple convnet that achieves ~99% test accuracy on MNIST.
Setup
import numpy as np
import keras
from keras import layers
Prepare the data
# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)
# Load the data and split it between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples
Build the model
model = keras.Sequential(
[
keras.Input(shape=input_shape),
layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Flatten(),
layers.Dropout(0.5),
layers.Dense(num_classes, activation="softmax"),
]
)
model.summary()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ conv2d (Conv2D) │ (None, 26, 26, 32) │ 320 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ max_pooling2d (MaxPooling2D) │ (None, 13, 13, 32) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ conv2d_1 (Conv2D) │ (None, 11, 11, 64) │ 18,496 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ max_pooling2d_1 (MaxPooling2D) │ (None, 5, 5, 64) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ flatten (Flatten) │ (None, 1600) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dropout (Dropout) │ (None, 1600) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dense (Dense) │ (None, 10) │ 16,010 │ └─────────────────────────────────┴───────────────────────────┴────────────┘
Total params: 34,826 (136.04 KB)
Trainable params: 34,826 (136.04 KB)
Non-trainable params: 0 (0.00 B)
Train the model
batch_size = 128
epochs = 15
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
Epoch 1/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 7s 9ms/step - accuracy: 0.7668 - loss: 0.7644 - val_accuracy: 0.9803 - val_loss: 0.0815
Epoch 2/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9627 - loss: 0.1237 - val_accuracy: 0.9833 - val_loss: 0.0623
Epoch 3/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9732 - loss: 0.0898 - val_accuracy: 0.9850 - val_loss: 0.0539
Epoch 4/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9761 - loss: 0.0763 - val_accuracy: 0.9880 - val_loss: 0.0421
Epoch 5/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9795 - loss: 0.0647 - val_accuracy: 0.9887 - val_loss: 0.0389
Epoch 6/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9824 - loss: 0.0580 - val_accuracy: 0.9903 - val_loss: 0.0345
Epoch 7/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9828 - loss: 0.0537 - val_accuracy: 0.9895 - val_loss: 0.0371
Epoch 8/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9838 - loss: 0.0503 - val_accuracy: 0.9907 - val_loss: 0.0340
Epoch 9/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9861 - loss: 0.0451 - val_accuracy: 0.9907 - val_loss: 0.0330
Epoch 10/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9866 - loss: 0.0427 - val_accuracy: 0.9917 - val_loss: 0.0298
Epoch 11/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9871 - loss: 0.0389 - val_accuracy: 0.9920 - val_loss: 0.0297
Epoch 12/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9885 - loss: 0.0371 - val_accuracy: 0.9912 - val_loss: 0.0285
Epoch 13/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9901 - loss: 0.0332 - val_accuracy: 0.9922 - val_loss: 0.0290
Epoch 14/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9885 - loss: 0.0340 - val_accuracy: 0.9923 - val_loss: 0.0283
Epoch 15/15
422/422 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9891 - loss: 0.0326 - val_accuracy: 0.9925 - val_loss: 0.0273
<keras.src.callbacks.history.History at 0x7f8497818af0>
Evaluate the trained model
score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])
Test loss: 0.02499214932322502
Test accuracy: 0.9919000267982483