I run successfull on notebook in Google but on my PC with 16GB RAM & Core I7 G10 computer is reboot, the same occurs with opencv
Thanks for your comments, i am beginner on Tensorflow an AI
is it necesary to do? Installation — TensorFlow 2 Object Detection API tutorial documentation
from tensorflow.python import training
import tensorflow as tf
from tensorflow import keras
import numpy as np
import os
import zipfile
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
local_zip = ‘rps.zip’
zip_ref = zipfile.ZipFile(local_zip,‘r’)
zip_ref.extractall(‘tmp/’)
zip_ref.close();
local_zip = ‘rps_validation.zip’
zip_ref = zipfile.ZipFile(local_zip,‘r’)
zip_ref.extractall(‘tmp/rps_validation/’)
zip_ref.close();
local_zip = ‘rps_test_set.zip’
zip_ref = zipfile.ZipFile(local_zip,‘r’)
zip_ref.extractall(‘tmp/’)
zip_ref.close();
TRAINING_DIR = “tmp/rps/”
TEST_DIR = “tmp/rps-test-set/”
VALIDATION_DIR = “tmp/rps_validation/”
training_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)
validation_datagen = ImageDataGenerator(rescale = 1./255)
train_generator =training_datagen.flow_from_directory(
TRAINING_DIR,
target_size=(150,150),
class_mode='categorical'
)
test_generator =test_datagen.flow_from_directory(
TEST_DIR,
target_size=(150,150),
class_mode='categorical'
)
validation_generator =validation_datagen.flow_from_directory(
VALIDATION_DIR,
target_size=(150,150),
class_mode='categorical'
)
#Imprimimos la segunda imagen del test
batch = next(test_generator)
img = batch[0][1]
plt.imshow(img)
model = tf.keras.models.Sequential ([
tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150,150,3)),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64,(3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64,(3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64,(3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(3, activation='softmax')
])
model.compile(optimizer=‘rmsprop’, loss=‘categorical_crossentropy’, metrics=[‘accuracy’])
history = model.fit_generator(train_generator, epochs=25, validation_data = validation_generator, verbose=1)
model.save(‘store/priedra_papel_tijera’)