tensorflow-hub

问题 I'm trying to build a CNN + RNN using a pre-trained model from tensorflow-hub: base_model = hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', input_shape=(244, 244, 3) base_model.trainable = False model = Sequential() model.add(TimeDistributed(base_model, input_shape=(15, 244, 244, 3))) model.add(LSTM(512)) model.add(Dense(256, activation='relu')) model.add(Dense(3, activation='softmax')) adam = Adam(learning_rate=learning_rate) model.compile(loss='categorical

问题 I'm trying to build a CNN + RNN using a pre-trained model from tensorflow-hub: base_model = hub.KerasLayer('https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4', input_shape=(244, 244, 3) base_model.trainable = False model = Sequential() model.add(TimeDistributed(base_model, input_shape=(15, 244, 244, 3))) model.add(LSTM(512)) model.add(Dense(256, activation='relu')) model.add(Dense(3, activation='softmax')) adam = Adam(learning_rate=learning_rate) model.compile(loss='categorical

问题 I have image classification problem and i want to use Keras pretrained models for this task. When I use such a model model = tf.keras.Sequential([ hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4", output_shape=[1280], trainable=False), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(num_classes, activation='softmax') ]) model.build([None, image_size[0], image_size[1], 3]) model.compile( optimizer=tf.keras.optimizers.Adam(), loss='categorical_crossentropy

问题 I have image classification problem and i want to use Keras pretrained models for this task. When I use such a model model = tf.keras.Sequential([ hub.KerasLayer("https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4", output_shape=[1280], trainable=False), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(num_classes, activation='softmax') ]) model.build([None, image_size[0], image_size[1], 3]) model.compile( optimizer=tf.keras.optimizers.Adam(), loss='categorical_crossentropy

问题 I have a tfrecord with 23 classes with 35 images in each class (805 in total). My current tfrecord read function is: def read_tfrecord(serialized_example): feature_description = { 'image': tf.io.FixedLenFeature((), tf.string), 'label': tf.io.FixedLenFeature((), tf.int64), 'height': tf.io.FixedLenFeature((), tf.int64), 'width': tf.io.FixedLenFeature((), tf.int64), 'depth': tf.io.FixedLenFeature((), tf.int64) } example = tf.io.parse_single_example(serialized_example, feature_description) image

问题 I have 140K sentences I want to get embeddings for. I am using TF_HUB Universal Sentence Encoder and am iterating over the sentences(I know it's not the best way but when I try to feed over 500 sentences into the model it crashes). My Environment is: Ubuntu 18.04 Python 3.7.4 TF 1.14 Ram: 16gb processor: i-5 my code is: version 1 I iterate inside the tf.session context manager embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder-large/3") df = pandas_repository.get

问题 I am trying to reproduce the output from a Tensorflow Hub module that is based on a Tensorflow Slim checkpoint, using the Tensorflow Slim modules. However, I can't seem to get the expected output. For example, let us load the required libraries, create a sample input and the placeholder to feed the data: import tensorflow_hub as hub from tensorflow.contrib.slim import nets images = np.random.rand(1,224,224,3).astype(np.float32) inputs = tf.placeholder(shape=[None, 224, 224, 3], dtype=tf

问题 I would like to get BERT embedding using tensorflow hub. I found it very easy to get ELMO embedding and my steps are below. Could anyone explain how to get BERT embedding on a windows machine? I found this but couldn't get it work on windows machine https://tfhub.dev/google/elmo/3 go to this link and then download. Unzip it twice till you see "tfhub_module.pb", provide path of that folder to get embedding import tensorflow as tf import tensorflow_hub as hub elmo = hub.Module("C:/Users/nnnn

问题 I want to use pre trained models in my go application. Especially the Inception-ResNet-v2 model. This model seems to be only available via tensorflow hub (https://www.tensorflow.org/hub/). However I could not find any documentation how to use tensorflow hub with the go language bindings for tensorflow. How can I download and use these models in go? 回答1: So after a lot of work in the past few days I finally found a way. At first I wanted to just use Python to do all the Tensorflow stuff and

问题 The script retrain.py described in How to Retrain an Image Classifier for New Categories was run as python retrain.py --tfhub_module https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/2 --image_dir /tmp/test and produced the output file /tmp/output_graph.pb . Converting this with tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model /tmp/output_graph.pb /tmp/model failed with IOError: SavedModel file does not exist at: /tmp/output_graph.pb/