11. Sieci neuronowe (Keras)

Keras to napisany w języku Python interfejs do platformy TensorFlow, służącej do uczenia maszynowego.

Aby z niego korzystać, trzeba zainstalować bibliotekę TensorFlow:

pip: https://www.tensorflow.org/install
conda: https://docs.anaconda.com/anaconda/user-guide/tasks/tensorflow

Przykład implementacji sieci neuronowej do rozpoznawania cyfr ze zbioru MNIST, według https://keras.io/examples/vision/mnist_convnet

# Konieczne importy

import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

# Przygotowanie danych

num_classes = 10
input_shape = (28, 28, 1)

# podział danych na zbiory uczący i testowy
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# skalowanie obrazów do przedziału [0, 1]
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# upewnienie się, że obrazy mają wymiary (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")

# konwersja danych kategorycznych na binarne
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 1s 0us/step
x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples

# Stworzenie modelu

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)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 1600)              0         
_________________________________________________________________
dropout (Dropout)            (None, 1600)              0         
_________________________________________________________________
dense (Dense)                (None, 10)                16010     
=================================================================
Total params: 34,826
Trainable params: 34,826
Non-trainable params: 0
_________________________________________________________________

# Uczenie modelu

batch_size = 128
epochs = 15

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit(x_train, y_train, epochs=1, batch_size=batch_size, epochs=epochs, validation_split=0.1)

422/422 [==============================] - 38s 91ms/step - loss: 0.0556 - accuracy: 0.9826 - val_loss: 0.0412 - val_accuracy: 0.9893

<tensorflow.python.keras.callbacks.History at 0x1a50b35a070>

# Ewaluacja modelu

score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

Test loss: 0.03675819933414459
Test accuracy: 0.988099992275238

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AITech — Uczenie maszynowe — laboratoria

11. Sieci neuronowe (Keras)

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