December 2017
Intermediate to advanced
536 pages
14h 23m
English
Content preview from TensorFlow 1.x Deep Learning Cookbook
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How to do it...
We proceed with the recipe as follows:
- We need to import the standard modules, TensorFlow, NumPy, and Pandas, for reading the .csv file, and Matplolib:
import tensorflow as tfimport numpy as npimport pandas as pdimport matplotlib.pyplot as plt
- The training, validation, and testing data is obtained using the helper functions:
X_train, Y_train = preprocess_data(train_data)X_val, Y_val = preprocess_data(val_data)X_test, Y_test = preprocess_data(test_data)
- Let us explore our data a little. We plot the mean image and find the number of images in each training, validation, and testing dataset:
# Explore Datamean_image = X_train.mean(axis=0)std_image = np.std(X_train, axis=0)print("Training Data set has {} images".format(len(X_train))) ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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