Actually what you’ve done is much more than simply copying weights. You made these two models identical all the time. Every time you update one model – the second one is also updated – as both models have the same weights variables. If you want to just copy weights – the simplest way is by … Read more
I had the same issue caused by last keras release,what i remember did(): 1-Upgrade tensorflow: pip install –user –upgrade tensorflow-gpu (there might be some missing packages, just pip install them) 2-Upgrade Tensorboard pip install –user –upgrade tensorboard (there might be some missing packages, just pip install them) 3-Downgrade Keras pip install keras==2.3.1 (latest version working … Read more
This works for me: $ brew install hdf5 $ export HDF5_DIR=”$(brew –prefix hdf5)” $ pip install –no-binary=h5py h5py
Keras is now fully intregrated into Tensorflow. So, importing only Keras causes error. It should be imported as: from tensorflow.keras.utils import to_categorical Avoid importing as: from keras.utils import to_categorical It is safe to use from tensorflow.keras. instead of from keras. while importing all the necessary modules. from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D,Dropout … Read more
For changing names of model.layers with tf.keras you can use the following lines: for layer in model.layers: layer._name = layer.name + str(“_2″) I needed this in a two-input model case and ran into the “AttributeError: can’t set attribute”, too. The thing is that there is an underlying hidden attribute _name, which causes the conflict.
You can use Keras model.summary() to get the name of the last layer. If model.outputs is not empty you can get the node names via: [node.op.name for node in model.outputs] you get the session via session = keras.backend.get_session() and you convert all training variables to consts via min_graph = convert_variables_to_constants(session, session.graph_def, [node.op.name for node in … Read more
If you are using the SummaryWriter from tensorboardX or pytorch 1.2, you have a method called add_scalars: Call it like this: my_summary_writer.add_scalars(f’loss/check_info’, { ‘score’: score[iteration], ‘score_nf’: score_nf[iteration], }, iteration) And it will show up like this: Be careful that add_scalars will mess with the organisation of your runs: it will add mutliple entries to this … Read more
The tensor must be passed to the layer when you are calling it, and not as an argument. Therefore it must be like this: x = Flatten()(x) # first the layer is constructed and then it is called on x To make it more clear, it is equivalent to this: flatten_layer = Flatten() # instantiate … Read more
TimeDistributedDense applies a same dense to every time step during GRU/LSTM Cell unrolling. So the error function will be between predicted label sequence and the actual label sequence. (Which is normally the requirement for sequence to sequence labeling problems). However, with return_sequences=False, Dense layer is applied only once at the last cell. This is normally … Read more
tensorflow.python.keras is just a bundle of keras with a single backend inside tensorflow package. This allows you to start using keras by installing just pip install tensorflow. keras package contains full keras library with three supported backends: tensorflow, theano and CNTK. If you even wish to switch between backends, you should choose keras package. This … Read more