![]() That seemed to solve the Value error I was encountering but is brought up another problem. I changed my imports to be: import tensorflow as tf I was importing from keras and tensorflow. I got past the error by changing the imports to be consistent. Here are the final few layers of the model architecture:īatch_normalization_11 (Batc (None, 256) 1024ĭoes this error have to do with the variable batch size or is there something else that I am missing from the tutorial? ValueError: Cannot infer num from shape (None, 2)įor reference I load in a model and then replace the final layers with a Dense(2) layer to try to be consistent with the tutorial. I keep getting this error when trying to use model.add(qlayer): I am trying to follow this tutorial to try and connect a quantum circuit to a pretrained CNN. Predict=model.predict_generator(test_generator) Let’s make a prediction on a test data using Keras’ predict_generator In : Score = model.evaluate_generator(valid_generator) Let’s evaluate our model performance In : Steps_per_epoch = train_generator.n//train_generator.batch_size, Let’s train the model using fit_generator: In : pile(loss="binary_crossentropy",optimizer="adam",metrics=) Model.add(Dense(2, activation='sigmoid')) Model.add(MaxPooling2D(pool_size=(2, 2))) Let’s define the Convolutional Neural Network (CNN) In : Test_generator = test_datagen.flow_from_directory( Valid_generator = image_datagen.flow_from_directory( ![]() Train_generator = image_datagen.flow_from_directory( Let’s initialize our training, validation and testing generator: In : Test_datagen = ImageDataGenerator(rescale=1 / 255.0) Let’s initialize Keras’ ImageDataGenerator class In : In :įrom keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropoutįrom import ImageDataGenerator Plt.imshow(img, cmap=plt.get_cmap('gray')) Img = plt.imread(os.path.join(path,str(i)+'.jpg')) The directory structure must be like as below: | - data 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. The syntax to call flow_from_directory() function is as follows: flow_from_directory(directory, target_size=(256, 256), color_mode='rgb', classes= None, class_mode='categorical', batch_size=32, shuffle= True, seed= None, save_to_dir= None, save_prefix='', save_format='png', follow_links= False, subset= None, interpolation='nearest') Prepare Datasetįor demonstration, we use the fruit dataset which has two types of fruit such as banana and Apricot. The below figure represents the directory structure: ![]() The test folder should contain a single folder, which stores all test images.The train folder should contain n sub-directories each containing images of respective classes.The root directory contains at least two folders one for train and one for the test.The directory structure is very important when you are using flow_from_directory() method. The flow_from_directory() method takes a path of a directory and generates batches of augmented data. ![]() This tutorial has explained flow_from_directory() 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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