Robototexnikada sun’iy intellekt texnologiyalari va vositasi fanidan

Amaliy qism


Dastur kodi
import tensorflow as tf
from tensorflow.keras import layers, models, datasets
import matplotlib.pyplot as plt


#MNIST datasetini yuklash
(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()


#Ma'lumotlarni oldindan qayta ishlash
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255


train_labels = tf.keras.utils.to_categorical(train_labels)
test_labels = tf.keras.utils.to_categorical(test_labels)


#Neyron tarmoq modelini tuzish
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10, activation='softmax')
])


# modelni kompilyatsiya qilish
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])


# Modelni o'qitish
history = model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_data=(test_images, test_labels))


#Modelni baholash
test_loss, test_acc = model.evaluate(test_images, test_labels)


print('Test accuracy:', test_acc)


# O'qitish tarixini ekranga chiqarish
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0, 1])
plt.legend(loc='lower right')
plt.show()
# Plot the training history
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0, 1])
plt.legend(loc='lower right')
plt.show()
Datasetni yuklash jarayoni





Modelni o’qitish tarixining visual ko’rinishi