I am trying to write my own data generator for a TensorFlow model and at this stage, I am only testing import numpy as np class OrderGenaratorForTransfomer(Sequence): n_products = 1000 n_windows = 10 n_customers = 10 def __init__( self ): pass def __getitem__(self, item): X = np.zeros((self.n_customers, self.n_windows, self.n_products)) Y = np.zeros((self.n_customers, 1, self.n_products)) return ..
Category : tensorflow
I am trying to use a pre-trained model which would skip some of the layers. There are 4 layers in a model, l1->l2->l3->l4, I need to bypass the output of l2 and feed it into l4 instead of l3 during inference for instance that is bringing the activations of the second conv layer all way ..
I’m designing a CNN for mnist dataset. Then it returns an error ValueError: Negative dimension size caused by subtracting 3 from 2 for ‘{{node conv2d_23/Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](batch_normalization_19/cond/Identity, conv2d_23/Conv2D/ReadVariableOp)’ with input shapes: [?,2,2,12], [3,3,12,12]. I tried to change padding = ‘valid’ to padding = ..
I need to make a model that has 2 dropout layers and two LSTM layers. Unfortunately I have a problem with input shape that goes to my second LSTM layer. After searching for the problem I found out I need to change the input dimensions but I don’t know how to do that. I found ..
I want to install tensorflow (and Keras too) for R. I used thoses lines install.packages("keras") install.packages("tensorflow") library(keras) library(tensorflow) And now I want to do : install_tensorflow() install_keras() But my python environement is inconsistent. Is there a way to told R to look for virtual python environnement ? Thanks Source: Python..
I am using the TF2 research object detection API with the pre-trained EfficientDet D3 model from the TF2 model zoo. During training on my own dataset I notice that the total loss is jumping up and down – for example from 0.5 to 2.0 a few steps later, and then back to 0.75: So all ..
I am using ssd_resnet50_v1_fpn_640x640_coco17_tpu-8, and when I train this model, Image Shown in TensorBoard are deformed. also in pipeline.config the size is set as 640×640 so I don’t understand why are the picture deformed. image_resizer { fixed_shape_resizer { height: 640 width: 640 } My input picture is 640×640 sized so it should not be deformed ..
Im attempting to create a custom object detection model using Tensorflow. I have all of the correct packages installed and using a jupyter notebook template to run the code (attached below) but once the model has completed training and openCV attempts to use the model in my device webcam i get errors but my camera ..
I have updated my tensorflow version by using pip command in command prompt: pip -upgrade tensorflow now I am getting this error while running my CNN model in Pycharm: ImportError: DLL load failed: The specified module could not be found. Failed to load the native TensorFlow runtime. See https://www.tensorflow.org/install/errors my python version = Python 3.7.0 ..
I am facing this issue while training the model. Everything seems fine but I cannot understand the problem. This is the error I am facing: InvalidArgumentError: Matrix size-incompatible: In[0]: [32,29], In[1]: [128,1] [[node gradient_tape/sequential_11/dense_23/MatMul (defined at <ipython-input-89-9b0f878131ef>:2) ]] [Op:__inference_train_function_10031] Function call stack: train_function Here is the model summary: Model: "sequential_11" _________________________________________________________________ Layer (type) Output Shape ..
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