Category "tensorflow"

File exists but Colab says no such file found

I mounted the files from google drive correctly with the following code. from google.colab import drive drive.mount('/content/gdrive') base_path = "/content/gdr

error: (-2:Unspecified error) Can't create layer "NoOp" of type "NoOp" in function 'cv::dnn::dnn4_v20210608::LayerData::getLayerInstance'

I am using tensorflow 2.4 and Cuda 11. I've build a CNN model from scratch using tensorflow and frozen it to create the .pb and .pbtxt file.Now I am trying to m

tf.data.Dataset, map functionality and random

Manipulating tf.data.Dataset I get a behavior, I am not able to understand the origin. I am manipulating a tf.data.Dataset a simple integer buffer where I want

Tensorflow dataset element shuffle within specified range

How do I shuffle the elements of tf.data.Dataset within a certain range. Having an input array, with shape = (10,), in the first 5 elements would be shuffled wi

No dependencies have been added to tensorflow.python.keras.engine.functional.Functional object

I was trying to load the checkpoints weights to the trained model and got the following error message. Any idea how to solve it? File "/src/trainer/loaders.

Saving a composite model that includes a custom layer results in error - None has NoneType, but expected one of: bytes, unicode

I'm trying to save a model which is a composite model of composite models. The first model is a sequential model of two sequential models. Both of the two sub-m

Error in loading image_dataset_from_directory in tensorflow?

This is the code from https://keras.io/examples/vision/image_classification_from_scratch/ import tensorflow as tf from tensorflow import keras from tensorflo

AttributeError: 'Model' object has no attribute '_distribution_strategy'

I'm trying to train a UNet, but for some reason I get the following error: Traceback (most recent call last): File "<ipython-input-54-b56497e81356>", l

'str' object has no attribute 'decode' for tensorflow in colab?

I'm a beginner of deeplearning.I copied the code with python3 in colab. Code is from the book called"Book - Practical Deep Learning for Cloud, Mobile & Edge

Sort Tensorflow HashTable by value

My Code : h_table = tf.lookup.StaticHashTable( initializer=tf.lookup.KeyValueTensorInitializer( keys=[0, 1, 2, 3, 4, 5], values=[12.3,

Apply Tensorflow tf.keras.initializers.GlorotNormal(seed=1) to tf.Variable

How to apply the initializer to the tf.Variable function? Am I on the right track? def initialize_parameters(): initializer

Reshape tensorflow tensors from feature columns into training samples

Currently my dataset looks like: feat_1 = tf.random.uniform( shape=[8000,1], minval=0, maxval=1, dtype=tf.dtypes.float32, seed=1123, nam

Keras ValueError: Dimensions must be equal - How to pass label-dependent values to custom loss function

I have a keras model with 5 outputs. My labels include 5 values to compare these to, but also 25 additional values representing a correlation matrix for the 5 v

Evaluate model result for multitask learning with keras

I designed a CNN for a multitask classification in keras, where I have one input and two different class of classes in output. I compiled the model in this way

ERROR : could not find a version that satisfies the requirement tensorflow

I'm using raspberry model 3B+ , I made a venv and then tried to install tensorflow but I get these 2 errors ERROR: Could not find a version that satisfies the r

Evaluate model result for multitask learning with keras

I designed a CNN for a multitask classification in keras, where I have one input and two different class of classes in output. I compiled the model in this way

Why is cuda-gdb much slower than gdb in executing the same program without breakpoints in CUDA kernels?

I am having trouble using cuda-gdb. My program starts from python and it loads a shared library containing tensorflow and cuda code. The command I used to start

model.fit in a for loop, for K-fold cross validation

I am trying to code a K-fold cross validation with LSTM architecture. But I got an this error (edit): Traceback (most recent call last): File "/Users/me/Deskt

Keras, TF: Do I have to label all images when adding an attribute to a mutilabel image classification model?

i have a dataset of images and built a strong image recognition model. now i want to add another label to my model. i am asking myself, if i have to label every

Repeated values in prediction with sequential model

The problem I got is with the result, I get the same value in the 'future' field in all the rows as follows. open high low close

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