![]() ![]() For example, in this post, the user is describing the exact behavior you are expecting. ![]() I think the documentation can be quite confusing and I imagine the behavior is different depending on your Tensorflow and Keras version. So basically, my question is how to use this generator correctly with function fit to have all data in my training set, including original, non-augmented images and augmented images, and to cycle through it several times/steps (right now it seems it does only one step per epoch)? Does it somehow infer number of steps? Also, does it use only augmented data, or it also uses non-augmented images in batch? When passing an infinitely repeating dataset, you must specify the steps_per_epoch argument. I am a bit confused how it works, because train_aug_ds is generator, so it should give infinitely big dataset. Train_aug_ds = train_aug.flow_from_directory(Īnd to train my model I did the following: model.fit(Īnd it worked. So, I loaded my data like this: train_aug = ImageDataGenerator( I also checked few blogs about using it, but they don't answer all my questions. I learned the hard way it is actually a generator, not iterator (because type(train_aug_ds) gives I thought it is an iterator). I am playing with augmentation of data in Keras lately and I am using basic ImageDataGenerator. ![]() Predict = model.predict_generator(test_generator) ![]() Let’s make a prediction on test data using Keras’ predict_generator: In : Score = model.evaluate_generator(valid_generator) Model.fit_generator(train_aug, validation_data=(testX, testY), pile(loss="binary_crossentropy",optimizer="adam",metrics=) Model.add(Dense(1, activation='sigmoid')) Model.add(MaxPooling2D(pool_size=(2, 2))) Let’s prepare a convolutional neural network (CNN). Test_generator = test_datagen.flow(test_data, batch_size=1) Valid_generator = train_datagen.flow(trainX, trainY, batch_size=batch_size) Train_generator = train_datagen.flow(trainX, trainY, batch_size=batch_size) Let’s create our train generator, validation generator and test generator In : Let’s initialize the Keras’ ImageDataGenerator class In : (trainX, valX, trainY, valY) = train_test_split(train_data, train_label, test_size=0.20, random_state=42) Train_label = to_categorical(train_label) Train_label = le.fit_transform(train_label) Test_data = np.array(test_data, dtype="float") / 255.0 Train_data = np.array(train_data, dtype="float") / 255.0 Let’s normalized each pixel values to the range and encode the target label. Img = cv2.imread(os.path.join(src_path,e)) Let’s load the train images and test images. In :įrom keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropoutįrom import ImageDataGeneratorįrom sklearn.preprocessing import LabelEncoderįrom sklearn.model_selection import train_test_split Plt.imshow(img, cmap=plt.get_cmap('gray')) Img = plt.imread(os.path.join(path,str(i)+'.jpg')) we need to train a classifier which can classify the input fruit image into class Banana or Apricot. Each class contain 50 images. You can download the dataset here and save & unzip it in your current working directory. Prepare Datasetįor demonstration, we use the fruit dataset which has two types of fruit such as banana and Apricot. This tutorial has explained flow() function with example. These three functions are:Įach of these function is achieving the same task to loads the image dataset in memory and generates batches of augmented data, but the way to accomplish the task is different. Keras’ ImageDataGenerator class provide three different functions to loads the image dataset in memory and generates batches of augmented data. You can also refer this Keras’ ImageDataGenerator tutorial which has explained how this ImageDataGenerator class work. If you do not have sufficient knowledge about data augmentation, please refer to this tutorial which has explained the various transformation methods with examples. Keras’ ImageDataGenerator class allows the users to perform image augmentation while training the model. ![]()
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