h5_to_pb.py from keras.models import load_model import tensorflow as tf import os import os.path as osp from keras import backend as K #路径参数 input_path = 'input path' weight_file = 'weight.h5' weight_file_path = osp.join(input_path,weight_file) outpu
import numpy as np np.random.seed(1337) # for reproducibility from keras.models import Sequential from keras.layers import Dense from keras.models import load_model # create some data X = np.linspace(-1, 1, 200) np.random.shuffle(X) # randomize the d
Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder_8:0", shape=(3, 3, 128, 256), dtype=float32) is not an element of this graph. 后端我使用的是django框架,上传一张图片传入基于tensorflow的keras模型进行预测,重复预测时,报告上述错误.原因大概是第二次预测时,model底层tensorflow的sessio
Applications Keras Applications are deep learning models that are made available alongside pre-trained weights. These models can be used for prediction, feature extraction, and fine-tuning. Weights are downloaded automatically when instantiating a mo
1.需要加上如下设置,否则转换前后输出可能不一致,这个主要针对dropout.BN层训练测试不一致 from keras import backend as K K.set_learning_phase(0) # 0 testing, 1 training mode 2.outputs而非output,否则会导致转换后无法 batch inference def h5_to_pb(h5_model, output_dir, model_name, out_prefix="output_"
I'm using keras 2.1.* with tensorflow 1.13.* backend. I save my model during training with .h5 format and after that I convert it into protobuf (.pb) model. Everything looks good during converting process, but the result of tensorflow model is a bit
开始 Keras 序列模型(Sequential model) 序列模型是一个线性的层次堆栈. 你可以通过传递一系列 layer 实例给构造器来创建一个序列模型. The Sequential model is a linear stack of layers. You can create a Sequential model by passing a list of layer instances to the constructor: from keras.models import Se
简单地训练一个四层全连接网络. Ref: http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ 1. Load Data 数据简介:Pima Indians Diabetes Data Set 下载 :Data download --> 保存为:pima-indians-diabetes.csv from keras.models import Sequential from keras.lay
原文:https://www.cnblogs.com/nowornever-L/p/6991295.html 1. TensorFlow 生成的 .ckpt 和 .pb 都有什么用? The .ckpt is the model given by tensorflow which includes all the weights/parameters in the model. The .pb file stores the computational graph. To make ten
1. 从 meta 模型恢复graph, 修改node 并保存 from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow.python.framework import graph_util # create a session sess = tf.Se
