tensorflow-estimator

问题 Context I have a colab with a very simple demo Estimator for the purpose of learning / understanding the Estimator API with the goal of making a convention for a plug-and-play model with useful bells and whistles of the trade in tack (e.g. early stopping if the validation set stops improving, exporting the model, etc). Each of the three Estimator modes ( TRAIN , EVAL , and PREDICT ) return an EstimatorSpec. According to the docs: __new__( cls, mode, predictions=None, # required by PREDICT

问题 Context I have a colab with a very simple demo Estimator for the purpose of learning / understanding the Estimator API with the goal of making a convention for a plug-and-play model with useful bells and whistles of the trade in tack (e.g. early stopping if the validation set stops improving, exporting the model, etc). Each of the three Estimator modes ( TRAIN , EVAL , and PREDICT ) return an EstimatorSpec. According to the docs: __new__( cls, mode, predictions=None, # required by PREDICT

问题 First steps in tensorflow, I'm trying to train a DNN model for image classification. My current code is: folder_path = Path('cropped_images/cropped') df['filename'] = df['tag_id'].map(lambda tag: str(folder_path / (tag + '.png'))) def database_input_fn(): def parse_image(filename, label): image_decoded = tf.image.decode_png(tf.read_file(filename), channels=3) image_resized = tf.image.resize_images(image_decoded, [64, 64]) label = label == 'large vehicle' return image_resized, label filenames

问题 I used the tutorial code from https://www.tensorflow.org/tutorials/recurrent_quickdraw and all works fine until I tried to make a prediction instead of just evaluate it. I wrote a new input function for prediction, based on the code in create_dataset.py def predict_input_fn(): def parse_line(stroke_points): """Parse an ndjson line and return ink (as np array) and classname.""" inkarray = json.loads(stroke_points) stroke_lengths = [len(stroke[0]) for stroke in inkarray] total_points = sum

问题 I want to use feedable iterator design in tensorflow Dataset API, so I can switch to validation data after some training steps. But if I switched to validation data, it will end the whole session. The following code demonstrate what I want to do: import tensorflow as tf graph = tf.Graph() with graph.as_default(): training_ds = tf.data.Dataset.range(32).batch(4) validation_ds = tf.data.Dataset.range(8).batch(4) handle = tf.placeholder(tf.string, shape=[]) iterator = tf.data.Iterator.from

问题 I want to use feedable iterator design in tensorflow Dataset API, so I can switch to validation data after some training steps. But if I switched to validation data, it will end the whole session. The following code demonstrate what I want to do: import tensorflow as tf graph = tf.Graph() with graph.as_default(): training_ds = tf.data.Dataset.range(32).batch(4) validation_ds = tf.data.Dataset.range(8).batch(4) handle = tf.placeholder(tf.string, shape=[]) iterator = tf.data.Iterator.from

问题 I am using tf.estimator.Estimator to train a model: def model_fn(features, labels, mode, params, config): input_image = features["input_image"] eval_metric_ops = {} predictions = {} # Create model with tf.name_scope('Model'): W = tf.Variable(tf.zeros([784, 10]), name="W") b = tf.Variable(tf.zeros([10]), name="b") logits = tf.nn.softmax(tf.matmul(input_image, W, name="MATMUL") + b, name="logits") loss = None train_op = None if mode != tf.estimator.ModeKeys.PREDICT: loss = tf.reduce_mean(tf.nn

问题 I would like to manage my training with a tf.estimator.Estimator but have some trouble to use it alongside the tf.data API. I have something like this: def model_fn(features, labels, params, mode): # Defines model's ops. # Initializes with tf.train.Scaffold. # Returns an tf.estimator.EstimatorSpec. def input_fn(): dataset = tf.data.TextLineDataset("test.txt") # map, shuffle, padded_batch, etc. iterator = dataset.make_initializable_iterator() return iterator.get_next() estimator = tf.estimator

问题 at the moment I try to train a autoencoder on a custom image dataset using the new Estimator API of Tensorflow. So far everything is working. The only problem I have is to save the input and output images as summary when the model is in evaluation mode. All image summaries I create in train mode are stored and shown in Tensorboard properly. Here is my code: def model_fn_autoencoder(features, labels, mode, params): is_training = mode == ModeKeys.TRAIN # Define model's architecture logits =

问题 at the moment I try to train a autoencoder on a custom image dataset using the new Estimator API of Tensorflow. So far everything is working. The only problem I have is to save the input and output images as summary when the model is in evaluation mode. All image summaries I create in train mode are stored and shown in Tensorboard properly. Here is my code: def model_fn_autoencoder(features, labels, mode, params): is_training = mode == ModeKeys.TRAIN # Define model's architecture logits =