I am currently doing an assignment on deep learning by downloading the assignment files from github.
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
%matplotlib inline
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).
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
I ran the setup.sh file too but the error doesn't seem to go away.
lr_utils.py file:
import numpy as np
import h5py
def load_dataset():
train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
Kindly help!
I solved the issue by downloading uncorrupted .h5 files and putting them in the folder datasets/ in the same directory.
The files you downloaded are corrupted. You can visit https://github.com/abdur75648/Deep-Learning-Specialization-Coursera to download the uncorrupted files.
you can download uncorrupted files from here :
https://www.kaggle.com/datasets/muhammeddalkran/catvnoncat
and replace it in the directory of the corrupted files
Related
I am trying to convert few camera-clicked images of handwritten Gujarati characters to the form of MNIST dataset as I intend to pass the Gujarati handwritten characters images to the MNIST deep learning model. And as part of that, I'm trying to assign a file path to a variable named "datadir". But when executing the below code in Ubuntu 16.04, the terminal throws the error which looks like this:
File "gujaratinn.py", line 7
datadir = /home/cryptoaniket256/Desktop/opencv-3.4.1/project/Resize
^
SyntaxError: invalid syntax
Note that the name of the file is gujaratinn.py and all the camera-clicked images are stored in the Resize folder.
import numpy as np
import matplotlib.pyplot as py
import os
import cv2
from pathlib import Path
datadir = Path("/home/cryptoaniket256/Desktop/opencv-
3.4.1/project/Resize")
fileToOpen = datadir/"practice.txt"
f = open(fileToOpen)
print(f.read())
Are you affecting datadir with a path you wrote on 2 rows in your code ?
Try to put line 7 and 8 on the same row or change the quotes like that:
import numpy as np
import matplotlib.pyplot as py
import os
import cv2
from pathlib import Path
datadir = Path("""/home/cryptoaniket256/Desktop/opencv-3.4.1/project/Resize""")
fileToOpen = datadir/"practice.txt"
f = open(fileToOpen)
print(f.read())
I am training my system for texture analysis, using local binary pattern. here I am training images. taken code from somewhere. I am getting the error in defining the path of images.
# OpenCV bindings
import cv2
# To performing path manipulations
import os
# Local Binary Pattern function
from skimage.feature import local_binary_pattern
# To calculate a normalized histogram
from scipy.stats import itemfreq
from sklearn.preprocessing import normalize
# Utility package -- use pip install cvutils to install
import cvutils
# To read class from file
import csv
#Store the path of training images in train_images
train_images = cvutils.imlist ("'C:\Users\Babar\MATLAB\isp\training
images\fire-image1.jpg',
'C:\Users\Babar\MATLAB\isp\training images\fire-image2.jpg',
'C:\Users\Babar\MATLAB\isp\training images\fire-image3.jpg'")
# Dictionary containing image paths as keys and corresponding label as
value
train_dic = {'fire-image1':0,'fire-image2':0,'fire-image3':0}
with open('C:\Users\Babar\MATLAB\isp\class_train.txt', 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=' ')
for row in reader:
train_dic[row[0]] = int(row[1])
I am facing the problem in reading images from multiple directory in python. Like there is single image in png format and it is located in multiple folders. I want to keep a for loop on that and then access the image like from every folder. So what could be the way to read those images from the particular folder?
import os
from os import listdir
from os.path import isfile, join
import sys
import cv2
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import glob
%matplotlib inline
mypath='E:/Datasets/CBIS-DDSM PNG/Converted_Test'
onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]
images = np.empty(len(onlyfiles), dtype=object)
for n in range(0, len(onlyfiles)):
images[n] = cv2.imread( join(mypath,onlyfiles[n]) )
Maybe you should try another method to reach to the directories for reading images.
in imread() you can pass the directory .so you can use str() and + to combine dynamic directories and fixed directory.
Here is the example maybe help you :
path = '/home/pictures/1'
for i in range(2) :
image = cv2.imread(str(path)+'1'+'/222.jpg')
plt.imshow(image)
plt.show()
In this example I had 2 folder 11 and 12 so with a for loop I changed folders that I need.
I am writing machine learning code that can be trained locally or in the cloud. I am using keras.preprocessing to load images, which under the hood uses PIL. It works fine for local files, but understandably doesn't understand Google Cloud Storage paths, like "gs://...".
from keras.preprocessing import image
image.load_img("gs://myapp-some-bucket/123.png")
Gives this error:
.../lib/python2.7/site-packages/keras/preprocessing/image.py", line 320, in load_img img = pil_image.open(path) File
.../lib/python2.7/site-packages/PIL/Image.py", line 2530, in open fp = builtins.open(filename, "rb") IOError: [Errno 2] No such file or directory: 'gs://myapp-some-bucket/123.png'
What is the correct way of doing this? I ultimately need a folder of images to be a single numpy array (images decoded and grayscale).
Found a replacement for keras.preprocessing.image.load_img, that understands GCS. I also included more code to read the whole folder, and turn every image in the folder into a single numpy array for training...
import os
import tensorflow as tf
from tensorflow.python.platform import gfile
filelist = gfile.ListDirectory("gs://myapp-some-bucket")
sess = tf.Session()
with sess.as_default():
x = np.array([np.array(tf.image.decode_png(tf.read_file(os.path.join(train_files_dir, filename))).eval()) for filename in filelist])
Load image:
image_path = 'gs://xxxxxxx.jpg'
image = tf.read_file(image_path)
image = tf.image.decode_jpeg(image)
image_array = sess.run(image)
Save image:
job_dir = 'gs://xxxxxxxx'
image = tf.image.encode_jpeg(image_array)
file_name = 'xxx.jpg'
write_op = tf.write_file(os.path.join(job_dir, file_name), image)
sess.run(write_op)
I'm trying to run this machine learning tree algorithm code in IPython:
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
iris = load_iris()
X = iris.data[:, 2:] # petal length and width
y = iris.target
tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X, y)
from sklearn.tree import export_graphviz
export_graphviz(tree_clf, out_file=image_path("iris_tree.dot"),
feature_names=iris.feature_names[2:],
class_names=iris.target_names,
rounded=True,
filled=True
)
But I get this error when run in IPython:
I'm unfamiliar with export_graphviz, does anyone have any idea how to correct this?
I guess you are following "Hands on Machine Learning with Scikit-Learn and TensorFlow" book by Aurelien Geron. I encountered with the same problem while trying out "Decision Trees" chapter. You can always refer to his GitHub notebooks . For your code, you may refer "decision tree" notebook.
Below I paste the code from notebook. Please do go ahead and have a look at the notebook also.
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "decision_trees"
def image_path(fig_id):
return os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID, fig_id)
def save_fig(fig_id, tight_layout=True):
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(image_path(fig_id) + ".png", format='png', dpi=300)
To get rid of all the mess simply remove image_path,
now out_file="iris_tree.dot", after running that command a file will be saved in your folder named iris_tree. Open that file in Microsoft Word and copy all of its content. Now open your browser and type "webgraphviz" and then click on the first link. Then delete whatever is written in white space and paste your code which is copied from iris_tree. Then click "generate graph". Scroll down and your graph is ready.
I know you might have got what you were looking for. But in case you don't, all you need to do is just replace:
out_file=image_path("iris_tree.dot")
with:
out_file="iris_tree.dot"
This will create the .dot file in the same directory in which your current script is.
You can also give the absolute path to where you want to save the .dot file as:
out_file="/home/cipher/iris_tree.dot"
you must correct
out_file=image_path("iris_tree.dot"),
in below code line:
out_file="C:/Users/VIDA/Desktop/python/iris_tree.dot",
You can directly type instead of using the webgraphviz, if you are using sklearn version 0.20.
import graphviz
with open ("iris_tree.dot") as f:
dot_graph = f.read()
display (graphviz.Source(dot_graph))
With sklearn 0.22 you have to change again. See sklearn users guide.
I have a sklearn with the version of 0.20.1, and I got the example to work through the line below.
export_graphviz(
tree_clf,
out_file = "iris_tree.dot",
feature_names = iris.feature_names[2:])