I was learning making custom layers in tensor flow but could not find out how to add trainable weights for example class Linear(layers.Layer): def __init__(self, units = 32, **kwargs): super().__init__(kwargs) self.units = units def build(self, input_shape): self.layer = layers.Dense(self.units, trainable= True) super().build(input_shape) def call(self, inputs): return self.layer(inputs) Now if I do linear_layer = Linear(8) x ..
I am using a tf.custom_ops called PyroNN. I wrapped this operator inside of a tf.keras.layers.Layer to use it in a model created with the functional API. The problem is, that this operator does not use tensorflows gpu memory management and consumes a lot of memory. This leads to errors like GPUassert: Out of memory and ..
How can I make a stateful custom layer, a layer that has a state whose value is updated at each batch I tried something Like this. class customLayer(Layer): … def build(self, input_shape): self.state = tf.Variable(…,trainable=False) def call(self, inputs): … K.update(self.state, new_state) return … the problem with this is that it can’t calculate the gradients of ..
I am training my models and I have written some Keras custom callbacks, which I wrap in a tf.keras.callbacks.CallbackList and call using the .on_train/epoch/batch_begin/end() functions. These functions take two arguments, epoch/batch and logs, which is a dictionary containing some ad hoc values that you would set, as far as I understand, using the high level ..
belos is my code to ensure that the folder has images, but tf.keras.preprocessing.image_dataset_from_directory returns no images found. What did I do wrong? Thanks. DATASET_PATH = pathlib.Path(‘C:UsersxxxDocumentsimages’) image_count = len(list(DATASET_PATH.glob(‘.*.jpg’))) print(image_count) output = 2715 batch_size = 4 img_height = 32 img_width = 32 train_ds = tf.keras.preprocessing.image_dataset_from_directory( DATASET_PATH.name, validation_split=0.8, subset="training", seed=123, image_size=(img_height, img_width), batch_size=batch_size) output: Found 0 ..
I’m trying to build a CNN model in order to classify an image, but whenever the training is done and I try to feed it a single image (from the training dataset) it misclassifies this image always. Please take a look at the code I wrote below. Thank you in advance. First, I declared an ..
I am following this code on GitHub. Its aim is to get a Monte Carlo Dropout for a LSTM model in order to produce confidence intervals. The code runs fine until this point, and I believe it’s because I’m on a much more recent version of Tensorflow and Keras. predict_stochastic = K.function([model.layers.input, K.learning_phase()], [model.layers[-1].output]) How ..
I have trained a model for Rock Paper Scissor hand gesture recognizing. When I tried to predict using model it gives a value error. I really confused trying to find a solution and I didnt. Hope someone will help me out My code : from time import sleep from keras.preprocessing.image import img_to_array from keras.preprocessing import ..
I’m Designing a Named Entity Recognition model. I used Data Generator to pass batch data into model.fit API. So in the process each batch contains samples with similar length but length differs for different batches. Vocabulary size of the data set is 35180. The maximum length of a training sample is 80. Rather than padding ..
I have a Convolutional Neural Network in the shape of the simple LeNet-5. I am using the modified NIST data set as my inputs Here is a picture of the structure Here’s my question: I want to take the output of conv2d and divide everything by 2 or some other number before inputting it to ..