mnist_convnet.md

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.

View in Colab • GitHub source

---

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

</div>
---
## Build the model


```python
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>

</div>
---
## Evaluate the trained model


```python
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

</div>