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 the necessary libraries, as follows:
from keras.applications.inception_v3 import InceptionV3from keras.preprocessing.image import ImageDataGeneratorfrom keras.models import Sequential, Modelfrom keras.layeSequential import Dense, GlobalAveragePooling2Dfrom keras.optimizers import Adamfrom keras.callbacks import EarlyStopping, ModelCheckpoint
- After importing, we can set the hyperparameters:
batch_size = 32n_epochs = 50steps_per_epoch = 100
- Because of the size of the dataset, it's important to use a batch generator. Before creating the generators, we will define Keras's ImageDataGenerator for preprocessing purposes:
train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, horizontal_flip=True, ...
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