Python Deep Learning Projects

Performing feature extraction

Now we will pre-process each image with the deep CNN encoder and dump the output to the disk:

1. We will load the images in batches using the load_image() helper function that we created before
2. We will feed the images into the encoder to extract the features
3. Dump the features as a numpy array:

encode_train = sorted(set(img_name_vector))#Load imagesimage_dataset = tf.data.Dataset.from_tensor_slices(                                encode_train).map(load_image).batch(16)# Extract featuresfor img, path in image_dataset:  batch_features = image_features_extract_model(img)  batch_features = tf.reshape(batch_features,                               (batch_features.shape[0], -1, batch_features.shape[3]))#Dump into disk  for bf, p in zip(batch_features, path): path_of_feature = p.numpy().decode("utf-8") ...