tensorflow-datasets

问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

问题 I had lost my dataset by a careless mistake. I have only my tflite file left in my hand. Is there any solution to reverse back h5 file. I have been done decent research in this but no solutions found. 回答1: The conversion from a TensorFlow SaveModel or tf.keras H5 model to .tflite is an irreversible process. Specifically, the original model topology is optimized during the compilation by the TFLite converter, which leads to some loss of information. Also, the original tf.keras model's loss and

问题 I have a python data generator- import numpy as np import tensorflow as tf vocab_size = 5 def create_generator(): 'generates sequences of varying lengths(5 to 7) with random number from 0 to voca_size-1' count = 0 while count < 5: sequence_len = np.random.randint(5, 8) # length varies from 5 to 7 seq = np.random.randint(0, vocab_size, (sequence_len)) yield seq count +=1 gen = tf.data.Dataset.from_generator(create_generator, args=[], output_types=tf.int32, output_shapes = (None, ), ) for g in

来源： https://stackoverflow.com/questions/58004272/is-there-a-reasonable-way-to-create-tff-clients-datat-sets

来源： https://stackoverflow.com/questions/55211315/memory-leak-with-tf-data

来源： https://stackoverflow.com/questions/55211315/memory-leak-with-tf-data

来源： https://stackoverflow.com/questions/55211315/memory-leak-with-tf-data