Pop!_OS allows easy installation and management of Tensorflow using 'tensorman'.
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| Installing TensorFlow on Pop!_OS using Tensorman |
First, make sure you have all the updates installed:
sudo apt update sudo apt full-upgrade
Then, install the tensorman package:
sudo apt install tensorman
In order to get Nvidia CUDA support, install the nvidia docker package:
sudo apt install nvidia-docker2
If you have been using data generators in Keras, such as ImageDataGenerator for augment and load the input data, then you would be familiar with the using the *_generator() methods (fit_generator(), evaluate_generator(), etc.) to pass the generators when trainning the model.
But recently, if you have switched to TensorFlow 2.1 or later (and tf.keras), you might have been getting a warning message such as,
Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version. Instructions for updating: Please use Model.fit, which supports generators.
Or,
Model.evaluate_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version. Instructions for updating: Please use Model.evaluate, which supports generators.
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| fit_generator() Deprecation Warning |
This is because in tf.keras, as well as the latest version of multi-backend Keras, the model.fit() function can take generators as well.
Traceback (most recent call last): File "lenet_mnist_keras.py", line 163, in <module> graph_training_history(history) File "lenet_mnist_keras.py", line 87, in graph_training_history plt.plot(history.history['acc']) KeyError: 'acc'
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| The KeyError: 'acc' when attempting to read the history object |
Traceback (most recent call last): File "lenet_mnist_keras.py", line 163, in <module> graph_training_history(history) File "lenet_mnist_keras.py", line 88, in graph_training_history plt.plot(history.history['val_acc']) KeyError: 'val_acc'
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| The KeyError: 'val_acc' when attempting to read the history object |
conda create --name tensorflow2 python=3.7 anaconda
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| Create a new conda environment |
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| TensorFlow Lite Logo |
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| The 'image_data_format' parameter in the keras.json file |
{
"epsilon": 1e-07,
"floatx": "float32",
"image_data_format": "channels_last",
"backend": "tensorflow"
}
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| An image classification system built with transfer learning |
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| The interface of Machine UI (Note: This is a screenshot from their announcement video) |
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| The keras.json file houses the configuration options for Keras |
ValueError: The shape of the input to "Flatten" is not fully defined (got (0, 7,
50). Make sure to pass a complete "input_shape" or "batch_input_shape" argument
to the first layer in your model.
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| Visual Studio 2015 installed with C++ |
conda create --name tensorflow numpy scipy scikit-learn pillow h5py mingw libpython
activate tensorflow
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| A representation of a Tensor Image courtesy: Wikipedia - https://commons.wikimedia.org/wiki/File:Components_stress_tensor.svg |
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| The keras.json file contains the Keras configuration options |
{
"image_dim_ordering": "tf",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}
{
"image_dim_ordering": "th",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "theano"
}