As suggested by François Chollet (creator of Keras) in his book, one way to handle missing values is to replace them with zero: In general, with neural networks, it’s safe to input missing values as 0, with the condition that 0 isn’t already a meaningful value. The network will learn from exposure to the data … Read more
I found an answer myself by using Keras functional API from keras.applications import VGG16 from keras.layers import Dropout from keras.models import Model model = VGG16(weights=”imagenet”) # Store the fully connected layers fc1 = model.layers[-3] fc2 = model.layers[-2] predictions = model.layers[-1] # Create the dropout layers dropout1 = Dropout(0.85) dropout2 = Dropout(0.85) # Reconnect the layers … Read more
Here’s a solution that’s a bit shorter and faster. If your one-hot encoded y is a np.array: import numpy as np from sklearn.utils.class_weight import compute_class_weight y_integers = np.argmax(y, axis=1) class_weights = compute_class_weight(‘balanced’, np.unique(y_integers), y_integers) d_class_weights = dict(enumerate(class_weights)) d_class_weights can then be passed to class_weight in .fit.
Unfortunately what you want to do is impossible with Keras … I’ve also struggle a lot of time on this problems and the only way is to dive into the rabbit hole and work with Tensorflow directly to do LSTM rolling prediction. First, to be clear on terminology, batch_size usually means number of sequences that … Read more
If you use Keras, use CuDNNLSTM in place of LSTM or CuDNNGRU in place of GRU. In my case (2 Tesla M60), I am seeing 10x boost of performance. By the way I am using batch size 128 as suggested by @Alexey Golyshev.
Answer from @Korakot works for a single file and in case, to delete entire folder or subfolders or files use !rm -rf <folder_name>
I ran into the same issue. After changing: from tensorflow import keras to: import keras life is once again worth living.
Let me explain it via an example: So let’s say you have the following series: 1,2,3,4,5,6,…,100. You have to decide how many timesteps your lstm will learn, and reshape your data as so. Like below: if you decide time_steps = 5, you have to reshape your time series as a matrix of samples in this … Read more
This would not be a standard approach to deal with unbalanced data. Nor do I think it would be really justified – you would be significantly changing the distributions of your classes, where the smaller class is now much less variable. The larger class would have rich variation, the smaller would be many similar images … Read more
Looks like you can use Slim Example: import numpy as np from tensorflow.python.layers import base import tensorflow as tf import tensorflow.contrib.slim as slim x = np.zeros((1,4,4,3)) x_tf = tf.convert_to_tensor(x, np.float32) z_tf = tf.layers.conv2d(x_tf, filters=32, kernel_size=(3,3)) def model_summary(): model_vars = tf.trainable_variables() slim.model_analyzer.analyze_vars(model_vars, print_info=True) model_summary() Output: ——— Variables: name (type shape) [size] ——— conv2d/kernel:0 (float32_ref 3x3x3x32) [864, … Read more