InvalidArgumentError: Graph execution error: We typically call this method layers data augmentation due to the fact that the Sequential class we use for data augmentation is the same class we use for implementing sequential neural networks (e.g., LeNet, VGGNet, AlexNet). Below is my code:-import cv2 import numpy as np import os import tensorflow as tf import random folderpath './real/' files os.listdir(folderpath) def getimage(): index random.randint(0,len(files)-1) img cv2.imread(folderpath+filesindex) img cv2.resize(img. usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)ĥ4 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,ĥ6 except core._NotOkStatusException as e: Incorporating data augmentation into a tf.data pipeline is most easily achieved by using TensorFlow’s preprocessing module and the Sequential class. I'm using the batch(8) function, it modifies the shape and adds batch dimension, but only getting one image per batch. > 13 history_first = model.fit(train_set, epochs=5) I am referring to this tf.data example where it has been shown how. Bazel version (if compiling from source): GCC/Compiler version (if compiling from source): CUDA/cuDNN version: GPU model and memory: Describe the current behavior. tf. provides a very convenient way to use the python generators for consuming the dataset. InvalidArgumentError Traceback (most recent call last) TensorFlow installed from (source or binary): TensorFlow version (use command below): 2.0.0. Using tf.: Here we will be using the tf. method which is quite efficient as compared to the previously discussed tf. method.
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