tensorflow

问题 I am new to the concept of Similarity Learning. I am currently doing a face recognition model using Siamese Neural Network for the Labelled Faces in the Wild Dataset. Code for Siamese Network Model (Consider each code snippets to be a cell in Colab): from keras.applications.inception_v3 import InceptionV3 from keras.applications.mobilenet_v2 import MobileNetV2 from keras.models import Model from keras.layers import Input,Flatten def return_inception_model(): input_vector=Input((224,224,3))

问题 i work on thhe example based in this web and here is i got after this jobs_train, jobs_test = jobs_df.randomSplit([0.6, 0.4]) >>> zuckerberg_train, zuckerberg_test = zuckerberg_df.randomSplit([0.6, 0.4]) >>> train_df = jobs_train.unionAll(zuckerberg_train) >>> test_df = jobs_test.unionAll(zuckerberg_test) >>> from pyspark.ml.classification import LogisticRegression >>> from pyspark.ml import Pipeline >>> from sparkdl import DeepImageFeaturizer >>> featurizer = DeepImageFeaturizer(inputCol=

问题 I am running the following in a browser: INDEX.HTML (BODY) <script src="https://unpkg.com/@tensorflow/tfjs"></script> <script src="https://unpkg.com/@tensorflow/tfjs-automl"></script> <img id="daisy" crossorigin="anonymous" src="https://storage.googleapis.com/tfjs-testing/tfjs-automl/img_classification/daisy.jpg" /> <script> async function run() { const model = await tf.automl.loadImageClassification("model.json"); const image = document.getElementById("daisy"); const predictions = await

问题 I see code like this in TF: from tensorflow.python.eager import function ... class _PerDeviceGenerator(dataset_ops.DatasetV2): """A `dummy` generator dataset.""" def __init__(self, shard_num, multi_device_iterator_resource, incarnation_id, source_device, element_spec): ... # TODO(b/124254153): Enable autograph once the overhead is low enough. @function.defun(autograph=False) # Pure graph code. def _remote_init_func(): return functional_ops.remote_call( target=source_device, args=init_func

问题 Preliminary information: The answers given for the previously asked related questions (e.g., How to change the backend of Keras to Theano?, How to switch Backend with Keras (from TensorFlow to Theano)) did not work for me. Hence, I've created my own question. As the title clearly describes, I need to change the backend of Keras dynamically (per run). How can I do this? I'm using Python 3 . Here is a function that I've created through the feedback on the web: from keras import backend as K

问题 I trained my model using tf.keras. I convert this model to '.pb' by, import os import tensorflow as tf from tensorflow.keras import backend as K K.set_learning_phase(0) from tensorflow.keras.models import load_model model = load_model('model_checkpoint.h5') model.save('model_tf2', save_format='tf') This creates a folder 'model_tf2' with 'assets', varaibles, and saved_model.pb I'm trying to load this model in cpp. Referring to many other posts (mainly, Using Tensorflow checkpoint to restore

问题 I trained my model using tf.keras. I convert this model to '.pb' by, import os import tensorflow as tf from tensorflow.keras import backend as K K.set_learning_phase(0) from tensorflow.keras.models import load_model model = load_model('model_checkpoint.h5') model.save('model_tf2', save_format='tf') This creates a folder 'model_tf2' with 'assets', varaibles, and saved_model.pb I'm trying to load this model in cpp. Referring to many other posts (mainly, Using Tensorflow checkpoint to restore

问题 Imagine a typical auto-encoder-decoder model. However, instead of a general decoder where deconvoutions together with upscaling are used to create/synthesize a tensor similar to the model's input, I need to implement a structured/custom decoder. Here, I need the decoder to take its input, e.g. a 10x2 tensor where each row represents x,y positions or coordinates, and render a fixed predefined size image where there are 10 gaussian distributions generated at the location specified by the input.

问题 Consider the following code: import numpy as np A = np.array([[.8, .6], [.1, 0]]) B1 = tf.keras.utils.normalize(A, axis=0, order=1) B2 = tf.keras.utils.normalize(A, axis=0, order=2) print('A:') print(A) print('B1:') print(B1) print('B2:') print(B2) which returns A: [[0.8 0.6] [0.1 0. ]] B1: [[0.88888889 1. ] [0.11111111 0. ]] B2: [[0.99227788 1. ] [0.12403473 0. ]] I understand how B1 is computed via order=1 such that each entry in A is divided by the sum of the elements in its column. For

问题 Consider the following code: import numpy as np A = np.array([[.8, .6], [.1, 0]]) B1 = tf.keras.utils.normalize(A, axis=0, order=1) B2 = tf.keras.utils.normalize(A, axis=0, order=2) print('A:') print(A) print('B1:') print(B1) print('B2:') print(B2) which returns A: [[0.8 0.6] [0.1 0. ]] B1: [[0.88888889 1. ] [0.11111111 0. ]] B2: [[0.99227788 1. ] [0.12403473 0. ]] I understand how B1 is computed via order=1 such that each entry in A is divided by the sum of the elements in its column. For