How to Make an Image Classifier - Intro to Deep Learning #6
Why Stack Overflow recommends it
Stack Overflow users link it mainly as a source for a CNN image classifier tutorial, and several questions and answers cite it as background when asking about or explaining convolutional networks and the code it uses.
Recommended by 1 people across 1 different months, 2017 to 2019.
What it is
Siraj Raval's 8-minute beginner video from his Intro to Deep Learning series builds a cat and dog image classifier in about 40 lines of Python. It covers a short history of image classification and the ideas behind convolutional neural networks, and links a GitHub coding challenge and a list of CNN learning resources.
What people said
Recommends CodingYourLife · 2017-06 · Stack Overflow · on “Error in ipynb file for neural net”
Check again minute 2:45 [this video] You are missing from parser import load_data But this seems to be a custom module that is not provided. What you want is to read all files from a folder into an array/list. Here is a code that can do that to get you started: import os from PIL import Image import cv2 images = [] images2 = [] folder="somepics" for filename in os.listdir(folder): if not filename.startswith('.'): print(filename) #read with PIL img = Image.open("{0}/{1}".format(folder, filename)) print(type(img)) images.append(img) #read with cv2 img2 = cv2.imread(filename) print(type(img2)) images2.append(img) Remember that images need to be of same size. You might need to reshape them.
Cites Amajid Sinar · 2017-05 · Stack Overflow · on “Audio Feature for Deep Learning”
I managed to classify time series data using Convolutional Neural Network . Convolution Neural Network is basically the same as Artificial Neural Network. The only difference is that, the input to the ANN must be convolved first to extract specific features. In an intuitive way, convolution operation basically highlights specific features of some data. It is best depicted by flashlight shined through different parts of the images. By doing so, we can highlight specific features of the image. That's the main idea of CNN. It is inherently designed to extract spatial features. The convolution operation is usually stacked, which means you have (row,column,dimensions) so the output of the convolu
Cites Amajid Sinar · 2017-05 · Stack Overflow · on “Overview for Deep Learning Networks”
There are basically two types of neural networks, supervised and unsupervised learning. Both need a training set to "learn". Imagine training set as a massive book where you can learn specific information. In supervised learning, the book is supplied with answer key but without the solution manual, in contrast, unsupervised learning comes without answer key or solution manual. But the goal is the same, which is that to find patterns between the questions and answers (supervised learning) and questions (unsupervised learning). Now we have differentiate between those two, we can go into the models. Let's discuss about supervised learning, which basically has 3 main models: artificial neural ne
Cites Garrett Schneider · 2017-06 · Stack Overflow · on “Error in ipynb file for neural net”
Im kind of a noob, so bear with me here. I have been watching Siraj Raval on youtube, and tried to use the neural net he created in this video [this video] . I have two sepparate folders of images, one of cells containg malaria and the other of healthy cells. I as not sure what to use for validation data. I get an error after I use the load_data function on the path for where the images are contained, and I'm not sure how to fix this. It seems like one of the packages im using that is supposed to have that function is not working properly. I have coppied the code from each cell (Im using jupyter notebook) into this text file. The long spaces in between the text are to show they are in different c
Cites Juan Ramos · 2019-04 · Stack Overflow · on “How to feed a value for a placeholder in keras/tensorflow”
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Dropout,Activation,Flatten, Conv2D, MaxPooling2D import pickle import keras as ks x1 =pickle.load(open("dX.pickle","rb")) y2 =pickle.load(open("dY.pickle","rb")) nx = x1/255.0 model = Sequential() model.add(Conv2D(64,(3,3),input_shape = nx.shape[1:])) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(64,3,3)) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size = (2,2))) model.add(Flatten()) model.add(Dense(64)) model.add(Dense(1)) model.add(Activation('sigmoid')) model.compile(loss = "binary_crossentropy", optimizer ="adam", metrics