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Robototexnikada sun’iy intellekt texnologiyalari va vositasi fanidan
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| bet | 4/4 | | Sana | 16.12.2023 | | Hajmi | 1,88 Mb. | | #120341 |
Bog'liq 5-amaliy robo Kompyuter tarmoqlari 1- mustaqil ish, 1702618603, tarmoq -5-amaliy, 1702125915 (1), 4-amaliy ML, test machine and robo, 2-mavzu (MO\'t turlari)(40-56). docx (2), 5-amaliy ML, 1702125915 (9), CaCrXYRJ72GHOxmxh4u9m25jn83bJGTlrLYTdcyb (1), 1699369989, Untitled document.edited, transport ekologiyasi, 1700107053Amaliy 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
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