keras

How do I use the Tensorboard callback of Keras?

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keras.callbacks.TensorBoard(log_dir=”./Graph”, histogram_freq=0, write_graph=True, write_images=True) This line creates a Callback Tensorboard object, you should capture that object and give it to the fit function of your model. tbCallBack = keras.callbacks.TensorBoard(log_dir=”./Graph”, histogram_freq=0, write_graph=True, write_images=True) … model.fit(…inputs and parameters…, callbacks=[tbCallBack]) This way you gave your callback object to the function. It will be run during the training and … Read more

Many to one and many to many LSTM examples in Keras

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So: One-to-one: you could use a Dense layer as you are not processing sequences: model.add(Dense(output_size, input_shape=input_shape)) One-to-many: this option is not supported well as chaining models is not very easy in Keras, so the following version is the easiest one: model.add(RepeatVector(number_of_times, input_shape=input_shape)) model.add(LSTM(output_size, return_sequences=True)) Many-to-one: actually, your code snippet is (almost) an example of this … Read more

Can I run Keras model on gpu?

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Yes you can run keras models on GPU. Few things you will have to check first. your system has GPU (Nvidia. As AMD doesn’t work yet) You have installed the GPU version of tensorflow You have installed CUDA installation instructions Verify that tensorflow is running with GPU check if GPU is working sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) … Read more

What is the role of “Flatten” in Keras?

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If you read the Keras documentation entry for Dense, you will see that this call: Dense(16, input_shape=(5,3)) would result in a Dense network with 3 inputs and 16 outputs which would be applied independently for each of 5 steps. So, if D(x) transforms 3 dimensional vector to 16-d vector, what you’ll get as output from … Read more

Why is TensorFlow 2 much slower than TensorFlow 1?

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UPDATE 8/1730/2020: TF 2.3 has finally done it: all cases run as fast, or notably faster, than any previous version. Further, my previous update was unfair to TF; my GPU was to blame, has been overheating lately. If you see a rising stem plot of iteration times, it’s a reliable symptom. Lastly, see a dev’s … Read more

Tensorflow 2.0 – AttributeError: module ‘tensorflow’ has no attribute ‘Session’

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According to TF 1:1 Symbols Map, in TF 2.0 you should use tf.compat.v1.Session() instead of tf.Session() https://docs.google.com/spreadsheets/d/1FLFJLzg7WNP6JHODX5q8BDgptKafq_slHpnHVbJIteQ/edit#gid=0 To get TF 1.x like behaviour in TF 2.0 one can run import tensorflow.compat.v1 as tf tf.disable_v2_behavior() but then one cannot benefit of many improvements made in TF 2.0. For more details please refer to the migration guide … Read more

Where do I call the BatchNormalization function in Keras?

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Just to answer this question in a little more detail, and as Pavel said, Batch Normalization is just another layer, so you can use it as such to create your desired network architecture. The general use case is to use BN between the linear and non-linear layers in your network, because it normalizes the input … Read more

Why binary_crossentropy and categorical_crossentropy give different performances for the same problem?

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The reason for this apparent performance discrepancy between categorical & binary cross entropy is what user xtof54 has already reported in his answer below, i.e.: the accuracy computed with the Keras method evaluate is just plain wrong when using binary_crossentropy with more than 2 labels I would like to elaborate more on this, demonstrate the … Read more

Keras, How to get the output of each layer?

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You can easily get the outputs of any layer by using: model.layers[index].output For all layers use this: from keras import backend as K inp = model.input # input placeholder outputs = [layer.output for layer in model.layers] # all layer outputs functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs] # evaluation functions # Testing test … Read more

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