tf.keras

来源： https://stackoverflow.com/questions/60253082/weighting-samples-in-multiclass-image-segmentation-using-keras

来源： https://stackoverflow.com/questions/60930158/tensorflow-saving-subclass-model-which-has-multiple-arguments-to-call-method

来源： https://stackoverflow.com/questions/59972212/can-we-pass-a-dataset-of-different-format-that-the-training-one-for-evaluating-a

来源： https://stackoverflow.com/questions/59972212/can-we-pass-a-dataset-of-different-format-that-the-training-one-for-evaluating-a

来源： https://stackoverflow.com/questions/59972212/can-we-pass-a-dataset-of-different-format-that-the-training-one-for-evaluating-a

问题 I am reading in a csv file with pandas, and give the column names stored in colname colnames=['file', 'label'] # Read data from file data = pd.read_csv('./Hand_Annotations_2.csv',names=colnames, header=None) # Preview the first 5 lines of the loaded data data.head() Then, I use ImageDataGenerator() and flow_fromdataframe() to get batches of data train_generator=datagen.flow_from_dataframe(dataframe=data, directory=None, x_col=colnames[0], y_col=colnames[1], class_indices=IDmap, class_mode=

问题 I am reading in a csv file with pandas, and give the column names stored in colname colnames=['file', 'label'] # Read data from file data = pd.read_csv('./Hand_Annotations_2.csv',names=colnames, header=None) # Preview the first 5 lines of the loaded data data.head() Then, I use ImageDataGenerator() and flow_fromdataframe() to get batches of data train_generator=datagen.flow_from_dataframe(dataframe=data, directory=None, x_col=colnames[0], y_col=colnames[1], class_indices=IDmap, class_mode=

问题 I'm having some difficulty with chaining together two models in an unusual way. I am trying to replicate the following flowchart: For clarity, at each timestep of Model[0] I am attempting to generate an entire time series from IR[i] (Intermediate Representation) as a repeated input using Model[1] . The purpose of this scheme is it allows the generation of a ragged 2-D time series from a 1-D input (while both allowing the second model to be omitted when the output for that timestep is not

问题 I'm having some difficulty with chaining together two models in an unusual way. I am trying to replicate the following flowchart: For clarity, at each timestep of Model[0] I am attempting to generate an entire time series from IR[i] (Intermediate Representation) as a repeated input using Model[1] . The purpose of this scheme is it allows the generation of a ragged 2-D time series from a 1-D input (while both allowing the second model to be omitted when the output for that timestep is not

问题 An official tutorial on @tf.function says: To get peak performance and to make your model deployable anywhere, use tf.function to make graphs out of your programs. Thanks to AutoGraph, a surprising amount of Python code just works with tf.function, but there are still pitfalls to be wary of. The main takeaways and recommendations are: Don't rely on Python side effects like object mutation or list appends. tf.function works best with TensorFlow ops, rather than NumPy ops or Python primitives.