I trained a CNN in Keras with images in a folder (two types of bees). I have a second folder with unlabeled bees images for prediction.
I'm able to predict a single image (as per below code).
from keras.preprocessing import image
test_image = image.load_img('data/test/20300.jpg')
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
prob = classifier.predict_proba(test_image)
Result:
prob
Out[214]: array([[1., 0.]], dtype=float32)
I would like to be able to predict all of the images (around 300).
Is there a way to load and predict all the images in a batch? And will predict() be able to handle it, as it expects and array to predict?
Model.predict_proba() (which is a synonym of predict() really) accepts the batch input. From the documentation:
Generates class probability predictions for the input samples.
The input samples are processed batch by batch.
You just need to load several images and glue them together in a single numpy array. By expanding the 0 dimension your code already uses a batch of 1 in test_image. To complete the picture there's also a Model.predict_on_batch() method.
To load a batch of test images you can use image.list_pictures or ImageDataGenerator.flow_from_directory() (which is compatible with Model.predict_generator() method, see the examples in the documentation).
Related
from tensorflow.keras.preprocessing.image import ImageDataGenerator
With image data generator's flow_from_directory method can we reshape images also.
e.g. we have color images in 10 classes in 10 folders and we are providing path of that directory let's say train:
gen = ImageDataGenerator(rescale=1./255, width_shift_range=0.05, height_shift_range=0.05)
train_imgs= gen .flow_from_directory(
'/content/data/train',
target_size=(10,10),
batch_size=1,
class_mode='categorical')
Now my model is taking input shape 300. And I want to define training data from this train_imgs that is images of 10X10X3.
Is there any library, method or option available to convert this data generator to matrix in which columns are each image vector?
Generally the best option in these cases is to add a Reshape layer to the start of your model: layers.Reshape((300), input_shape=(10,10,3)). You can also do layers.Reshape((-1), input_shape=(10,10,3)), and it will automatically figure out the correct output length.
In order to do multiclass segmentation the masks need to be one-hot-encoded. For example if I have a 100 images of shape 224x224x3 with 5 different classes I would have a set of masks with shape (100, 224, 224, 5) i.e the last dimension (the channel) refers to the class of the pixel. Take a grayscale masks that contains 6 classes where each pixel has the label 1-6, I can easily convert this to the categorical mask I need using tf.keras.utils.to_categorical.
If I use the ImageDataGenerator provided with keras I know I can create a generator for both images and masks then zip them together for the problem (as code shows below) but where i'm confused is how do I convert the masks into this categorical one-hot-encoded structure whilst using the ImageDataGenerator? The ImageDataGenerator only finds files in directories that are saved as images therefore I can't convert the masks and then save them down as numpy arrays (the one-hot-encoded masks) for the generator to pick up, as images can't have that have more than 4 channels right? Is there somehow of telling the generator to do this conversion? Or does this therefore limit the number of classes I can have in my problem?
One solution is to write my own custom generator with the sequence class which I have done but I'm keen on understanding if this is possible to do with Keras inbuilt ImageDataGenenerator? Could writing my a lambda layer on the network be the solution?
mask_categorical = tf.keras.utils.to_categoricl(mask) #converts 224x224 grayscale mask to one-hot encoding version
imgDataGen = ImageDataGenerator(rescale=1/255.)
maskDataGen = ImageDataGenerator()
imageGenerator =imageDataGen.flow_from_directory("dataset/image/",
class_mode=None, seed=40)
maskGenerator = maskDataGen.flow_from_directory("dataset/mask/",
class_mode=None, seed=40)
trainGenerator = zip(imageGenerator, maskGenerator)
I have a model pretrained on ImageNet like this:
from keras.applications import resnet50
model = resnet50.ResNet50(weights='imagenet')
Is there any way to get test ImageNet dataset and their labels (which means data not used for training the above model)?
Original
Sadly ImageNet (from ILSVRC2012) never released the labels for the 100k images in the test dataset. You can use instead the 50k images from the Validation dataset, whose labels can be found in the Develpoment Kit (tasks 1 and 2).
You can download both the Validation images and the validation labels from http://www.image-net.org/challenges/LSVRC/2012/downloads
Updated!
There is an ImageNet Test set with 100k images available here, and although you cannot access the labels, you can instead predict the labels for all the images and submit your predictions for evaluation (top1, top5) here.
You can load a model with weights=None and download imagenet dataset.You can then split the dataset into train-test, train your model with train set and use test set for your purpose
I am trying to use a convolutional neural network to identify patterns in binary matrices and classify them to one of two classes. At the moment I have a bunch of 15x15 matrices in csv format.
In order to get a handle on how convolutional nets work I have been following sentdex's tutorials on youtube. In this he uses a conv net to classify the MNIST dataset. The code he uses to specify the input is like this:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')
My question is how do I set up a file like 'input_data' which the conv net can read my matrices and labels from? Can I include ALL of my training data in one file or do I need to split them into train/test files?
I have set up an excel file in the following format but not sure if it will work in the same way MNIST does.
input data example file:
My favorite tutorials are from aymericdamien, below is a link to the convolutional tutorial in jupyter (go back up a few directories in github for all of the tutorials).
https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb
You'll notice that their input is the same as what you have posted:
X = tf.placeholder(tf.float32, [None, num_input])
Y = tf.placeholder(tf.float32, [None, num_classes])
And the first thing they do in the conv_net() function is to reshape it to a image:
x = tf.reshape(x, shape=[-1, 28, 28, 1])
The shape arguments are understood as follows:
-1: variable batch size
28: height of the image (mnist is 28x28 grayscale images)
28: width of the image
1: color channels, grayscale images have 1 color channel, RGB images have 3 typically.
Try reshaping the image using numpy and displaying it yourself to check that you got it right:
import scipy.misc as misc
import numpy as np
img = np.reshape(flat_image, (28,28,1))
misc.imshow(img)
As far as train and test process goes, tensorflow doesn't care anything about your structure. I generally would separate the files to make sure you don't accidentally pass your test set to your training process though. You will ultimately need to call sess.run separately on your training and test datasets. I think the tutorial I linked to provides a very good example of this process, so if you have more specific questions I'll leave them to a future post.
I am working on the following keras convolutional neural network tutorial https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d
After training the model I want to test the model on sample images, and also label the images. I realize that I have to use predict method that generates an array that shows which label gets what score for a particular image. But I am having trouble using this method. If the images are in the folder test_images and there are 20 of them, how do I test these images and get the prediction?
This is how I've gotten with one image (even though I want it for multiple images):
image = cv2.imread('test1.jpg')
image = cv2.resize(image,(224,224))
features = np.swapaxes(np.swapaxes(image, 1, 2), 0, 1)
predictions = model.predict(features)
This throws the following error:
ValueError: Error when checking : expected conv2d_1_input to have 4 dimensions, but got array with shape (3, 224, 224)
Thank you very much!
Some of the questions I consulted before:
Simple Neural Network in Python not displaying label for the test image
https://github.com/fchollet/keras/issues/315
model.predict works by processing an array of samples, not just one image, so you are missing the batch/samples dimension, which in your case would only be just one image. You just have to reshape the array:
features = features.reshape((1, 3, 224, 224)
And then pass it to predict.