![]() This is why it is important to name every layer in the model, so we can access quickly and transparently any layer we need. To accomplish this, we do not need the final prediction, we need the output of an intermediate layer, specifically, the one we named latent_space on the model definition. It is time to use Latent Space to find similar images. Left: image input / Right: image reconstruction with the trained Autoencoder Once the model is fitted, we can try to reconstruct some images, since this is the objective of the Autoencoder: Validation_steps=validation_set.n // batch_size,Ĭallbacks = [ModelCheckpoint('models/image_autoencoder_2.h5', Steps_per_epoch=training_set.n // batch_size, Output = Activation('sigmoid', name='decoder')(decoder)Īutoencoder = Model(input_model, output, name='autoencoder')Īpile(loss="mse", optimizer= Adam(learning_rate=1e-3)) Latent_space = Dense(16, name='latent_space')(encoder)ĭecoder = Dense(np.prod(encoder_dim))(latent_space)ĭecoder = Reshape((encoder_dim, encoder_dim, encoder_dim))(decoder)ĭecoder = Conv2DTranspose(64, (3,3), padding='same', kernel_initializer='normal')(decoder)ĭecoder = BatchNormalization(axis=-1)(decoder)ĭecoder = Conv2DTranspose(32, (3,3), padding='same', kernel_initializer='normal')(decoder)ĭecoder = Conv2DTranspose(3, (3, 3), padding="same")(decoder) Input_model = Input(shape=(img_height, img_width, channels))Įncoder = Conv2D(32, (3,3), padding='same', kernel_initializer='normal')(input_model)Įncoder = BatchNormalization(axis=-1)(encoder)Įncoder = Conv2D(64, (3,3), padding='same', kernel_initializer='normal')(encoder)Įncoder = Conv2D(64, (3,3), padding='same', kernel_initializer='normal')(input_model) Now, we can define our model architecture, and fit it with the images: # Define the autoencoder In this example, I have the following images directory: flipkart/images/images/. – "input": images identical to input images (mainly used to work with autoencoders), – "multi_output": list with the values of the different columns, – "raw": numpy array of values in y_col column(s), – "sparse": 1D numpy array of integer labels, – None, no targets are returned (the generator will only yield batches of image data, which is useful to use in model.predict()).Īlso, for this to work, you should have all images inside another folder, so the Keras API assumes you have only one class. Mode for yielding the targets: - "binary": 1D numpy array of binary labels, – "categorical": 2D numpy array of one-hot encoded labels. class_mode: one of “binary”, “categorical”, “input”, “multi_output”, “raw”, sparse” or None.On the ImageDataGenerator documentation, we found the following: The argument class_mode='input' is the key here. Validation_set = train_datagen.flow_from_directory( Training_set = train_datagen.flow_from_directory( Train_datagen = ImageDataGenerator(rescale=1./255, from import ImageDataGenerator, load_img, img_to_array, array_to_imgįrom import Model, load_modelįrom import Flatten, Conv2D, Conv2DTranspose, LeakyReLU, BatchNormalization, Input, Dense, Reshape, Activationįrom import Adamįrom import ModelCheckpoint Here, we are going to use the ImageDataGenerator API. Once we have downloaded the images, we can define the training and validation set. ![]() To train the Autoencoder, we are going to use the Keras module inside the Tensorflow 2.0 library. If the model is robust, we can reduce all the complexity of the image to a small dimension.Īutoencoder architecture / Source: Comp Three Inc So if we can the input, we can reduce the dimension of the image, to a very small vector, and this vector is the Latent Space. The idea behind this model, is to reconstruct the input we feed the algorithm, so the input and output size is the same. This type of model consists of three main parts: Let’s try to get similar images, by using an Autoencoder model. The code for downloading images and developing both approaches is found on this Github repo. So we are going to find similar images from the products of this huge Indian e-commerce.Īfter downloading the images from the available URLs found on the data, we get 18322 images of different products. We are going to sol ve this problem using the Flipkart images dataset. We are going to compare two different approaches: ![]() In this post, we are going to develop and compare two different ways in which using Deep Learning algorithms we can solve this problem of querying between thousands of images, the most similar images. Imagine searching on the web for similar images to the one we are taking with our phones. Imagine that you want to search for similar images to any picture. Photo by Geran de Klerk on Unsplash Introduction ![]()
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