A Tensorflow data pipeline for data augmentation
Project details
I want to create a Tensorflow data pipeline that accepts two separate arrays of image data and the first shape is (364, 436, 3), and the second shape is (256, 512, 3) for a CNN classification having 564 training samples, 75 test samples and 75 test samples for the two sets of arrays.
A pipeline was already created to accept the individual arrays using “first_train_loader = tensorflow.data.Dataset.from_tensor_slices((first_array_target, first_array_label))”, “second_train_loader = tensorflow.data.Dataset.from_tensor_slices((second_array_target, second_array_label))”.
The individual arrays achieved over a 90% classification accuracy when tested and if done well the combination should improve.
Awarded to:
Avinash D.
(5.0)
Project budget: £20 GBP
budget limits: £20-250 GBP
number of bids: 20
average bids: £119
Awarded to:
Avinash D.
(5.0)
Project budget: £20 GBP
budget limits: £20-250 GBP
number of bids: 20
average bids: £119
Project details
I want to create a Tensorflow data pipeline that accepts two separate arrays of image data and the first shape is (364, 436, 3), and the second shape is (256, 512, 3) for a CNN classification having 564 training samples, 75 test samples and 75 test samples for the two sets of arrays.
A pipeline was already created to accept the individual arrays using “first_train_loader = tensorflow.data.Dataset.from_tensor_slices((first_array_target, first_array_label))”, “second_train_loader = tensorflow.data.Dataset.from_tensor_slices((second_array_target, second_array_label))”.
The individual arrays achieved over a 90% classification accuracy when tested and if done well the combination should improve.
A pipeline was already created to accept the individual arrays using “first_train_loader = tensorflow.data.Dataset.from_tensor_slices((first_array_target, first_array_label))”, “second_train_loader = tensorflow.data.Dataset.from_tensor_slices((second_array_target, second_array_label))”.
The individual arrays achieved over a 90% classification accuracy when tested and if done well the combination should improve.
Skills: Deep LearningNeural NetworksPythonTensorflow
