Tensorflow Documentation
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tensorflow
How to suppress verbose Tensorflow logging? [duplicate]
2.0 Update (10/8/19) Setting TF_CPP_MIN_LOG_LEVEL should still work (see below in v0.12+ update), but there is currently an issue open (see issue #31870). If setting TF_CPP_MIN_LOG_LEVEL does not work for you (again, see below), try doing the following to set the log level: import tensorflow as tf tf.get_logger().setLevel(‘INFO’) In addition, please see the documentation on … Read more
What is an epoch in TensorFlow?
An epoch, in Machine Learning, is the entire processing by the learning algorithm of the entire train-set. The MNIST train set is composed by 55000 samples. Once the algorithm processed all those 55000 samples an epoch is passed.
ImportError: cannot import name ‘to_categorical’ from ‘keras.utils’ (/usr/local/lib/python3.7/dist-packages/keras/utils/__init__.py)
use this from tensorflow.keras.utils import to_categorical instead of from keras.utils import to_categorical
How to quantize all nodes except a particular one?
EDIT: the previous answer refered to Tensorflow Lite code. I updated it to refer to Tensorflow. Looking at the implementation of Tensorflow’s quantize_weights, these are the instances where weights don’t get quantized: tensor that is not type float tensor that has fewer than 1024 weights (or another number specified by the parameter minimum_size) If you … Read more
Difference between `apply_gradients` and `minimize` of optimizer in tensorflow
You can easily know from the link : https://www.tensorflow.org/get_started/get_started (tf.train API part) that they actually do the same job. The difference it that: if you use the separated functions( tf.gradients, tf.apply_gradients), you can apply other mechanism between them, such as gradient clipping.
Create keras callback to save model predictions and targets for each batch during training
NOTE: this answer is outdated and only works with TF1. Check @bers’s answer for a solution tested on TF2. After model compilation, the placeholder tensor for y_true is in model.targets and y_pred is in model.outputs. To save the values of these placeholders at each batch, you can: First copy the values of these tensors into … Read more
Are tf.layers.dense() and tf.contrib.layers.fully_connected() interchangeable?
They are essentially the same, the later calling the former. However tf.contrib.fully_connected adds a few functionalities on top of dense, in particular the possibility to pass a normalization and an activation in the parameters, à la Keras. As noted by @wordforthewise, mind that the later defaults to tf.nn.relu. More generally, the TF API proposes (and … Read more
What is the difference of static Computational Graphs in tensorflow and dynamic Computational Graphs in Pytorch?
Both frameworks operate on tensors and view any model as a directed acyclic graph (DAG), but they differ drastically on how you can define them. TensorFlow follows ‘data as code and code is data’ idiom. In TensorFlow you define graph statically before a model can run. All communication with outer world is performed via tf.Session … Read more
Output from TensorFlow `py_func` has unknown rank/shape
Since py_func can execute arbitrary Python code and output anything, TensorFlow can’t figure out the shape (it would require analyzing Python code of function body) You can instead give the shape manually y.set_shape(inp.get_shape())