tensorflow

Can not squeeze dim[1], expected a dimension of 1, got 5

由 。_饼干妹妹 提交于 2021-02-11 09:41:29
问题 I tried different solutions but still facing the issue. Actually I am new in Ml/DL (python). In which case we face this error "Can not squeeze dim1, expected a dimension of 1, got 5"? Please help me here, what I am doing wrong here and what is correct Here is InvalidArgumentError Traceback (most recent call last) --------------------------------------------------------------------------- <ipython-input-9-0826122252c2> in <module>() 98 model.summary() 99 model.compile(loss='sparse_categorical

GitHub排名TOP30的机器学习开源项目

由 让人想犯罪 __ 提交于 2021-02-11 08:32:25
对于机器学习者来说，阅读开源代码并基于代码构建自己的项目，是一个非常有效的学习方法。看看以下这些Github上平均star为3558的开源项目，你错了哪些？ 1. FastText：快速文本表示和文本分类库（Github上有11786颗星，贡献者Facebook Research） 源码链接：https://github.com/facebookresearch/MUSE 2. Deep-photo-styletransfer：“Deep Photo Style Transfer” 这篇论文的源码和数据。（GitHub 9747颗星，论文来自于康奈尔大学的Fujun Luan） 源码链接：https://github.com/luanfujun/deep-photo-styletransfer 3. 用Python和命令行来实现的最简单的面部识别API（GitHub 8672颗星，贡献者Adam Geitgey） 源码链接：https://github.com/ageitgey/face_recognition 4. Magenta:利用机器智能生成音乐和美术艺术品（GitHub 8113颗星） 源码链接：https://github.com/tensorflow/magenta 5. Sonnet：基于TensorFlow的神经网络库（GitHub 573颗星

Implementing Attention in Keras

由 梦想的初衷 提交于 2021-02-11 07:24:18
问题 I am trying to implement attention in keras over a simple lstm: model_2_input = Input(shape=(500,)) #model_2 = Conv1D(100, 10, activation='relu')(model_2_input) model_2 = Dense(64, activation='sigmoid')(model_2_input) model_2 = Dense(64, activation='sigmoid')(model_2) model_1_input = Input(shape=(None, 2048)) model_1 = LSTM(64, dropout_U = 0.2, dropout_W = 0.2, return_sequences=True)(model_1_input) model_1, state_h, state_c = LSTM(16, dropout_U = 0.2, dropout_W = 0.2, return_sequences=True,

Implementing Attention in Keras

由 我只是一个虾纸丫 提交于 2021-02-11 07:24:11
问题 I am trying to implement attention in keras over a simple lstm: model_2_input = Input(shape=(500,)) #model_2 = Conv1D(100, 10, activation='relu')(model_2_input) model_2 = Dense(64, activation='sigmoid')(model_2_input) model_2 = Dense(64, activation='sigmoid')(model_2) model_1_input = Input(shape=(None, 2048)) model_1 = LSTM(64, dropout_U = 0.2, dropout_W = 0.2, return_sequences=True)(model_1_input) model_1, state_h, state_c = LSTM(16, dropout_U = 0.2, dropout_W = 0.2, return_sequences=True,

How to run inference using Tensorflow 2.2 pb file?

由 谁说胖子不能爱 提交于 2021-02-11 06:25:15
问题 I followed the website: https://leimao.github.io/blog/Save-Load-Inference-From-TF2-Frozen-Graph/ However, I still do not know how to run inference with frozen_func (see my code below). Please advise how to run inference using pb file in TensorFlow 2.2. Thanks. import tensorflow as tf def wrap_frozen_graph(graph_def, inputs, outputs, print_graph=False): def _imports_graph_def(): tf.compat.v1.import_graph_def(graph_def, name="") wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, []

I'm having trouble with the transition of Tensorflow Python to Tensorflow.js in regards to image preprocessing. What am I missing?

由 二次信任 提交于 2021-02-11 06:24:47
问题 I'm having trouble with the transition of Tensorflow Python to Tensorflow.js in regards to image preprocessing in Python single_coin = r"C:\temp\coins\20Saint-03o.jpg" img = image.load_img(single_coin, target_size = (100, 100)) array = image.img_to_array(img) x = np.expand_dims(array, axis=0) vimage = np.vstack([x]) prediction =model.predict(vimage) print(prediction[0]) I get the correct result [2.8914417e-05 3.5085387e-03 1.9252902e-03 6.2635467e-05 3.7389682e-03 1.2983804e-03 7.4157811e-04

How implement Batch Norm with SWA in Tensorflow?

由 元气小坏坏 提交于 2021-02-11 06:17:18
问题 I am using Stochastic Weight Averaging (SWA) with Batch Normalization layers in Tensorflow 2.2. For Batch Norm I use tf.keras.layers.BatchNormalization . For SWA I use my own code to average the weights (I wrote my code before tfa.optimizers.SWA appeared). I have read in multiple sources that if using batch norm and SWA we must run a forward pass to make certain data (running mean and st dev of activation weights and/or momentum values?) available to the batch norm layers. What I do not

How can I keep imports lightweight and still properly type annotate?

由 ∥☆過路亽．° 提交于 2021-02-11 06:14:38
问题 Tensorflow is a super heavy import. I want to import it only when it's needed. However, I have a model loading function like this: from typing import Dict, Any from keras.models import Model # Heavy import! Takes 2 seconds or so! # Model loading is a heavy task. Only do it once and keep it in memory model = None # type: Optional[Model] def load_model(config: Dict[str, Any], shape) -> Model: """Load a model.""" if globals()['model'] is None: globals()['model'] = create_model(wili.n_classes,

How implement Batch Norm with SWA in Tensorflow?

由 三世轮回 提交于 2021-02-11 06:13:24
问题 I am using Stochastic Weight Averaging (SWA) with Batch Normalization layers in Tensorflow 2.2. For Batch Norm I use tf.keras.layers.BatchNormalization . For SWA I use my own code to average the weights (I wrote my code before tfa.optimizers.SWA appeared). I have read in multiple sources that if using batch norm and SWA we must run a forward pass to make certain data (running mean and st dev of activation weights and/or momentum values?) available to the batch norm layers. What I do not

How can I keep imports lightweight and still properly type annotate?

由 家住魔仙堡 提交于 2021-02-11 06:13:12
问题 Tensorflow is a super heavy import. I want to import it only when it's needed. However, I have a model loading function like this: from typing import Dict, Any from keras.models import Model # Heavy import! Takes 2 seconds or so! # Model loading is a heavy task. Only do it once and keep it in memory model = None # type: Optional[Model] def load_model(config: Dict[str, Any], shape) -> Model: """Load a model.""" if globals()['model'] is None: globals()['model'] = create_model(wili.n_classes,
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