keras - w3toppers.com

How to add an attention mechanism in keras?

If you want to have an attention along the time dimension, then this part of your code seems correct to me: activations = LSTM(units, return_sequences=True)(embedded) # compute importance for each step attention = Dense(1, activation=’tanh’)(activations) attention = Flatten()(attention) attention = Activation(‘softmax’)(attention) attention = RepeatVector(units)(attention) attention = Permute([2, 1])(attention) sent_representation = merge([activations, attention], mode=”mul”) You’ve worked … Read more

Using pre-trained word2vec with LSTM for word generation

I’ve created a gist with a simple generator that builds on top of your initial idea: it’s an LSTM network wired to the pre-trained word2vec embeddings, trained to predict the next word in a sentence. The data is the list of abstracts from arXiv website. I’ll highlight the most important parts here. Gensim Word2Vec Your … Read more

How to display custom images in TensorBoard using Keras?

So, the following solution works well for me: import tensorflow as tf def make_image(tensor): “”” Convert an numpy representation image to Image protobuf. Copied from https://github.com/lanpa/tensorboard-pytorch/ “”” from PIL import Image height, width, channel = tensor.shape image = Image.fromarray(tensor) import io output = io.BytesIO() image.save(output, format=”PNG”) image_string = output.getvalue() output.close() return tf.Summary.Image(height=height, width=width, colorspace=channel, encoded_image_string=image_string) … Read more

How can I convert a trained Tensorflow model to Keras?

I think the callback in keras is also a solution. The ckpt file can be saved by TF with: saver = tf.train.Saver() saver.save(sess, checkpoint_name) and to load checkpoint in Keras, you need a callback class as follow: class RestoreCkptCallback(keras.callbacks.Callback): def __init__(self, pretrained_file): self.pretrained_file = pretrained_file self.sess = keras.backend.get_session() self.saver = tf.train.Saver() def on_train_begin(self, logs=None): if … Read more

ValueError: Output tensors to a Model must be the output of a TensorFlow `Layer`

I have found a way to work around to solve the problem. For anyone who encounters the same issue, you can use the Lambda layer to wrap your tensorflow operations, this is what I did: from tensorflow.python.keras.layers import Lambda; def norm(fc2): fc2_norm = K.l2_normalize(fc2, axis = 3); illum_est = tf.reduce_sum(fc2_norm, axis = (1, 2)); illum_est … Read more

Unexpected keyword argument ‘ragged’ in Keras

So I tried link above which you have mentioned teachable machine As it turns out model you have exported is from tensorflow.keras and not directly from keras API. These two are different. So while loading it might be using tf.ragged tensors that might not be compatible with keras API. Soulution to your issue: Don’t import … Read more

Confusion about keras Model: call vs. call vs. predict methods

Adding to @Dmitry Kabanov, they are similar yet they aren’t exactly the same thing. If you care about performance, need to look in to critical differences between them. model.predict model(x) loops over the data in batches which means means that predict() calls can scale to very large arrays. happens in-memory and doesn’t scale not differentiable … Read more

Early stopping with Keras and sklearn GridSearchCV cross-validation

[Answer after the question was edited & clarified:] Before rushing into implementation issues, it is always a good practice to take some time to think about the methodology and the task itself; arguably, intermingling early stopping with the cross validation procedure is not a good idea. Let’s make up an example to highlight the argument. … Read more

‘Tensor’ object has no attribute ‘lower’

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

Error “Keras requires TensorFlow 2.2 or higher”

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