Personally, I really like TensorFlow 2.0 - I like how the TensorFlow team has expanded the entire ecosystem and how interoperable they are, I like how they have really pushed the tf.keras integration and how easy it is now to plug tf.keras with the native TensorFlow modules. But what I like the most is the ability to customize my training loops ...
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import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('data/', one_hot=True) def weight_variable(shape, name):
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Get started with TensorFlow.NET¶. I would describe TensorFlow as an open source machine learning framework developed by Google which can be used to build neural networks and perform a variety of machine learning tasks. it works on data flow graph where nodes are the mathematical operations and...
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In this case, we’re setting a 50% sparsity, meaning that 50% of the weights will be zeroed. block_size — The dimensions (height, weight) for the block; sparse pattern in matrix weight tensors. block_pooling_type — The function to use to pool weights in the block. Must be AVG or MAX.
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import tensorflow as tf model = tf.keras.Sequential ( [ tf.keras.Input ( 4 ,), tf.keras.layers.Dense ( 3, activation= "tanh", name= "layer1" ), tf.keras.layers.Dense ( 4, activation= "relu", name= "layer2" ), tf.keras.layers.Dense ( 2, activation= "sigmoid" ,name= "layer3" ), ]) Build the model.
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Each key is one of the layers and contains a list of the weights and biases. If you use the caffe-to-tensorflow function to convert weights on your own, you will get a python dictionary of dictionaries (e.g. weights[‘conv1’] is another dictionary with the keys weights and biases).