March 2020
Beginner to intermediate
342 pages
8h 38m
English
Here is a convolutional neural network for CIFAR-10—all of it:
| | import numpy as np |
| | from keras.models import Sequential |
| | from keras.layers import Conv2D, Dropout, Dense |
| | from keras.layers import BatchNormalization, Flatten |
| | from keras.optimizers import Adam |
| | from keras.utils import to_categorical |
| | from keras.datasets import cifar10 |
| | |
| | (X_train_raw, Y_train_raw), (X_test_raw, Y_test_raw) = cifar10.load_data() |
| | X_train = X_train_raw / 255 |
| | X_test_all = X_test_raw / 255 |
| | X_validation, X_test = np.split(X_test_all, 2) |
| | Y_train = to_categorical(Y_train_raw) |
| | Y_validation, Y_test = np.split(to_categorical(Y_test_raw), 2) |
| | |
| | model = Sequential() |
| | |
| | model.add(Conv2D(16, ... |