# ## 2 - Overview of the Problem set ##
#
# **Problem Statement**: You are given a dataset ("data.h5") containing:
# - a training set of m_train images labeled as cat (y=1) or non-cat (y=0)
# - a test set of m_test images labeled as cat or non-cat
# - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).
#
# You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.
#
# Let's get more familiar with the dataset. Load the data by running the following code.
# In[3]:
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
# In[8]:
print(train_set_x_orig.shape)
# We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).
#
# Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images.
# In[4]:
# Example of a picture
index = 25
# ## 2 - Overview of the Problem set ##
#
# **Problem Statement**: You are given a dataset ("data.h5") containing:
# - a training set of m_train images labeled as cat (y=1) or non-cat (y=0)
# - a test set of m_test images labeled as cat or non-cat
# - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).
#
# You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.
#
# Let's get more familiar with the dataset. Load the data by running the following code.
# In[76]:
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset(
)
# We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).
#
# Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images.
# In[77]:
# Example of a picture
index = 25
plt.imshow(train_set_x_orig[index])
print("y = " + str(train_set_y[:, index]) + ", it's a '" +
classes[np.squeeze(train_set_y[:, index])].decode("utf-8") +
"' picture.")
# Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs.
def load_data():
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
return train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 12 20:56:37 2018
@author: Administrator
"""
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes =
load_dataset()