Ok, so think about it like this: You are providing a list of index values to index the provided tensor to get those slices. The first dimension of the indices you provide is for each index you will perform. Let’s pretend that tensor is just a list of lists. [[0]] means you want to get … Read more
Run the following command in the terminal: export CUDA_HOME=/usr/local/cuda-X.X Where you replace X.X by the first two digits of your version number (can be found out e.g. via nvcc –version).
After looking at the documentation and the code, I was not able to find a way to remove a Variable from the TRAINABLE_VARIABLES. Here is what happens: The first time tf.get_variable(‘weights’, trainable=True) is called, the variable is added to the list of TRAINABLE_VARIABLES. The second time you call tf.get_variable(‘weights’, trainable=False), you get the same variable … 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
tf.argmax(input, axis=None, name=None, dimension=None) Returns the index with the largest value across axis of a tensor. input is a Tensor and axis describes which axis of the input Tensor to reduce across. For vectors, use axis = 0. For your specific case let’s use two arrays and demonstrate this pred = np.array([[31, 23, 4, 24, … Read more
To handle the validation logs with a separate writer, you can write a custom callback that wraps around the original TensorBoard methods. import os import tensorflow as tf from keras.callbacks import TensorBoard class TrainValTensorBoard(TensorBoard): def __init__(self, log_dir=”./logs”, **kwargs): # Make the original `TensorBoard` log to a subdirectory ‘training’ training_log_dir = os.path.join(log_dir, ‘training’) super(TrainValTensorBoard, self).__init__(training_log_dir, **kwargs) … Read more
Based on what you said it sounds like you need a larger batch_size, and of course there are implications with that which could impact the steps_per_epoch and number of epochs. To solve for jumping-around A larger batch size will give you a better gradient and will help to prevent jumping around You may also want … Read more
Yeah, the syntax has changed in T2.0. Here’s the correct piece: tf.compat.v1.GraphDef() # -> instead of tf.GraphDef() tf.compat.v2.io.gfile.GFile() # -> instead of tf.gfile.GFile()
y_true and y_pred The tensor y_true is the true data (or target, ground truth) you pass to the fit method. It’s a conversion of the numpy array y_train into a tensor. The tensor y_pred is the data predicted (calculated, output) by your model. Usually, both y_true and y_pred have exactly the same shape. A few … Read more
I have the following understanding of this function keras.backend.function. I will explain it with the help of a code snippet from this. The part of code snippet is as follows final_conv_layer = get_output_layer(model, “conv5_3”) get_output = K.function([model.layers[0].input], [final_conv_layer.output, model.layers[-1].output]) [conv_outputs, predictions] = get_output([img]) In this code, there is a model from which conv5_3 layer is … Read more