tensorflow-estimator

问题 I'd like to speed up my training routine that uses the Estimator API with input_fn wrote using tf.data.Dataset . My implementation takes 2 second to prepare a batch of data and then runs training on GPU for 1 sec, and then start over preparing a batch. Which is really inefficient. I'm looking for a way to prepare the batches asynchronously and upload them to GPU to speed up the training. Or alternatively for a way to cache datasets between invocations of input_fn (the dataset.cache() doesn't

问题 I'd like to speed up my training routine that uses the Estimator API with input_fn wrote using tf.data.Dataset . My implementation takes 2 second to prepare a batch of data and then runs training on GPU for 1 sec, and then start over preparing a batch. Which is really inefficient. I'm looking for a way to prepare the batches asynchronously and upload them to GPU to speed up the training. Or alternatively for a way to cache datasets between invocations of input_fn (the dataset.cache() doesn't

问题 I'm stuck on one line of code and have been stalled on a project all weekend as a result. I am working on a project that uses BERT for sentence classification. I have successfully trained the model, and I can test the results using the example code from run_classifier.py. I can export the model using this example code (which has been reposted repeatedly, so I believe that it's right for this model): def export(self): def serving_input_fn(): label_ids = tf.placeholder(tf.int32, [None], name=

问题 I'm stuck on one line of code and have been stalled on a project all weekend as a result. I am working on a project that uses BERT for sentence classification. I have successfully trained the model, and I can test the results using the example code from run_classifier.py. I can export the model using this example code (which has been reposted repeatedly, so I believe that it's right for this model): def export(self): def serving_input_fn(): label_ids = tf.placeholder(tf.int32, [None], name=

问题 I'm using the estimator library of tensorflow on python. I want to train a student network by using a pre-trained teacher.I'm facing the following issue. train_input_fn = tf.estimator.inputs.numpy_input_fn( x={"x": train_data}, y=train_labels, batch_size=100, num_epochs=None, shuffle=True) student_classifier.train( input_fn=train_input_fn, steps=20, hooks=None) This code returns a generator object that is passed to a student classifier. Inside the generator, we have the inputs and labels (in

问题 Both Tensorflow Keras models and Tensorflow Estimators are able to train neural network models and use them to predict new data. They are both high-level APIs that sits on top of the low-level core TensorFlow API. So when should I use one over the other? 回答1: Background The Estimators API was added to Tensorflow in Release 1.1, and provides a high-level abstraction over lower-level Tensorflow core operations. It works with an Estimator instance, which is TensorFlow's high-level representation

问题 Both Tensorflow Keras models and Tensorflow Estimators are able to train neural network models and use them to predict new data. They are both high-level APIs that sits on top of the low-level core TensorFlow API. So when should I use one over the other? 回答1: Background The Estimators API was added to Tensorflow in Release 1.1, and provides a high-level abstraction over lower-level Tensorflow core operations. It works with an Estimator instance, which is TensorFlow's high-level representation

问题 I am trying to train a DNNClassifier in tensorflow Here is my code train_input_fn = tf.estimator.inputs.pandas_input_fn( x=X_train, y=y_train, batch_size=1000, shuffle = True ) nn_classifier = tf.estimator.DNNClassifier(hidden_units=[1300,1300,1300], feature_columns=X_train, n_classes=200) nn_classifier.train(input_fn = train_input_fn, steps=2000) Here is how y_train looks [450 450 450 ... 327 327 327] type : numpy.ndarray And here is how X_train looks [[ 9.79285 11.659035 1.279528 ... 1

问题 I have a saved model (a directory with model.pd and variables) and wanted to run predictions on a pandas dataframe. I've unsuccessfully tried a few ways to do this: Attempt 1: Restore the estimator from the saved model estimator = tf.estimator.LinearClassifier( feature_columns=create_feature_cols(), model_dir=path, warm_start_from=path) Where path is the directory that has a model.pd and variables folder. I got an error ValueError: Tensor linear/linear_model/dummy_feature1/weights is not

问题 THe following code i have below produce the regular tensorflow model but when i try to convert it to tensorflow lite it doesn't work, i followed the following documentations. https://www.tensorflow.org/tutorials/estimator/linear1 https://www.tensorflow.org/lite/guide/get_started export_dir = "tmp" serving_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn( tf.feature_column.make_parse_example_spec(feat_cols)) estimator.export_saved_model(export_dir, serving_input_fn) #