im doing deep learning stuff. I have tensorflow 2.0 (cpu version) and when i try to run this code(below) in pycharm (or jupyter notebook) it uses 3GBs of memory(RAM) however i have 6GBs of RAM
the dataset that i run has 50000+ training pic and 10k test pics (as i remember). the code is:
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np
data_mnist = keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) = data_mnist.load_data()
class_names = ['airplane', 'automobile', 'bird',
'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
]
x_train = x_train/255.0
x_test = x_test/255.0
model = keras.Sequential([
keras.layers.Flatten(input_shape=(32, 32, 3)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax'),
])
model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1)
image_label = 1
prediction = model.predict(x_test)
plt.grid(False)
plt.imshow(x_test[image_label])
plt.title('Actual Img: ' + class_names[image_label])
plt.xlabel('Predicted: ' + class_names[np.argmax(prediction[image_label])])
plt.show
it happens whenever i try to train the model.
there is no problem about the code, but as i said the computer almost crashes when i try to run this code(look at the picture below): enter image description here
i hope anybody can help just drop what u think will be the answer. Thanks a lot<3
