October 2017
Intermediate to advanced
330 pages
7h 7m
English
Content preview from Python Deep Learning Cookbook
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How to do it...
- We start by importing all libraries, as follows:
import numpy as npimport matplotlib.pyplot as pltfrom keras.models import Modelfrom keras.applications.vgg16 import VGG16from keras.layers import Dense, GlobalAveragePooling2Dfrom keras.optimizers import Adamfrom keras.applications import imagenet_utilsfrom keras.utils import np_utilsfrom keras.callbacks import EarlyStopping
- In this recipe, we will be using Keras' cifar10 dataset:
from keras.datasets import cifar10(X_train, y_train), (X_test, y_test) = cifar10.load_data()
- We need to preprocess the data before we feed it to our model:
n_classes = len(np.unique(y_train))y_train = np_utils.to_categorical(y_train, n_classes)y_test = np_utils.to_categorical(y_test, n_classes) ...
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