关于保存h5模型、权重网上的示例非常多，也非常简单。主要有以下两个函数：

1、keras.models.load_model() 读取网络、权重

2、keras.models.load_weights() 仅读取权重

load_model代码包含load_weights的代码，区别在于load_weights时需要先有网络、并且load_weights需要将权重数据写入到对应网络层的tensor中。

下面以resnet50加载h5权重为例，示例代码如下

import kerasfrom keras.preprocessing import imageimport numpy as npfrom network.resnet50 import ResNet50#修改过，不加载权重（默认官方加载亦可）model = ResNet50() # 参数默认 by_name = Fasle， 否则只读取匹配的权重# 这里h5的层和权重文件中层名是对应的（除input层）model.load_weights(r'\models\resnet50_weights_tf_dim_ordering_tf_kernels_v2.h5')

模型通过 model.summary()输出

一、模型加载权重 load_weights()

def load_weights(self, filepath, by_name=False, skip_mismatch=False, reshape=False): if h5py is None: raise ImportError('`load_weights` requires h5py.') with h5py.File(filepath, mode='r') as f: if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] if by_name: saving.load_weights_from_hdf5_group_by_name( f, self.layers, skip_mismatch=skip_mismatch,reshape=reshape) else: saving.load_weights_from_hdf5_group(f, self.layers, reshape=reshape)

这里关心函数saving.load_weights_from_hdf5_group(f, self.layers, reshape=reshape)即可，参数 f 传递了一个h5py文件对象。

读取h5文件使用 h5py 包，简单使用HDFView看一下resnet50的权重文件。

resnet50_v2 这个权重文件，仅一个attr “layer_names”, 该attr包含177个string的Array，Array中每个元素就是层的名字（这里是严格对应在keras进行保存权重时网络中每一层的name值，且层的顺序也严格对应）。

对于每一个key（层名），都有一个属性"weights_names"，（value值可能为空)。

例如：

conv1的"weights_names"有"conv1_W:0"和"conv1_b:0"，

flatten_1的"weights_names"为null。

这里就简单介绍，后面在代码中说明h5py如何读取权重数据。

二、从hdf5文件中加载权重 load_weights_from_hdf5_group()

1、找出keras模型层中具有weight的Tensor(tf.Variable)的层

def load_weights_from_hdf5_group(f, layers, reshape=False): # keras模型resnet50的model.layers的过滤 # 仅保留layer.weights不为空的层，过滤掉无学习参数的层 filtered_layers = [] for layer in layers: weights = layer.weights if weights: filtered_layers.append(layer)

filtered_layers为当前模型resnet50过滤（input、paddind、activation、merge/add、flastten等）层后剩下107层的list

2、从hdf5文件中获取包含权重数据的层的名字

前面通过HDFView看过每一层有一个[“weight_names”]属性，如果不为空，就说明该层存在权重数据。

先看一下控制台对h5py对象f的基本操作（需要的去查看相关数据结构定义）：

>>> f<HDF5 file "resnet50_weights_tf_dim_ordering_tf_kernels_v2.h5" (mode r)>>>> f.filename'E:\\DeepLearning\\keras_test\\models\\resnet50_weights_tf_dim_ordering_tf_kernels_v2.h5'>>> f.name '/'>>> f.attrs.keys() # f属性列表 #<KeysViewHDF5 ['layer_names']>>>> f.keys() #无顺序<KeysViewHDF5 ['activation_1', 'activation_10', 'activation_11', 'activation_12', ...,'activation_8', 'activation_9', 'avg_pool', 'bn2a_branch1', 'bn2a_branch2a', ...,'res5c_branch2a', 'res5c_branch2b', 'res5c_branch2c', 'zeropadding2d_1']>>>> f.attrs['layer_names'] #*** 有顺序, 和summary()对应 ****array([b'input_1', b'zeropadding2d_1', b'conv1', b'bn_conv1', b'activation_1', b'maxpooling2d_1', b'res2a_branch2a', ..., b'res2a_branch1', b'bn2a_branch2c', b'bn2a_branch1', b'merge_1', b'activation_47', b'res5c_branch2b', b'bn5c_branch2b', ..., b'activation_48', b'res5c_branch2c', b'bn5c_branch2c', b'merge_16', b'activation_49', b'avg_pool', b'flatten_1', b'fc1000'], dtype='|S15')>>> f['input_1']<HDF5 group "/input_1" (0 members)>>>> f['input_1'].attrs.keys() # 在keras中，每一个层都有‘weight_names'属性 #<KeysViewHDF5 ['weight_names']>>>> f['input_1'].attrs['weight_names'] # input层无权重 #array([], dtype=float64)>>> f['conv1']<HDF5 group "/conv1" (2 members)>>>> f['conv1'].attrs.keys()<KeysViewHDF5 ['weight_names']>>>> f['conv1'].attrs['weight_names'] # conv层有权重w、b #array([b'conv1_W:0', b'conv1_b:0'], dtype='|S9')

从文件中读取具有权重数据的层的名字列表

# 获取后hdf5文本文件中层的名字，顺序对应 layer_names = load_attributes_from_hdf5_group(f, 'layer_names') #上一句实现 layer_names = [n.decode('utf8') for n in f.attrs['layer_names']] filtered_layer_names = [] for name in layer_names: g = f[name] weight_names = load_attributes_from_hdf5_group(g, 'weight_names') #上一句实现 weight_names = [n.decode('utf8') for n in f[name].attrs['weight_names']] #保留有权重层的名字 if weight_names: filtered_layer_names.append(name) layer_names = filtered_layer_names # 验证模型中有有权重tensor的层 与 从h5中读取有权重层名字的 数量 保持一致。 if len(layer_names) != len(filtered_layers): raise ValueError('You are trying to load a weight file ' 'containing ' + str(len(layer_names)) + ' layers into a model with ' + str(len(filtered_layers)) + ' layers.')

3、从hdf5文件中读取的权重数据、和keras模型层tf.Variable打包对应

先看一下权重数据、层的权重变量(Tensor tf.Variable)对象，以conv1为例

>>> f['conv1']['conv1_W:0'] # conv1_W:0 权重数据数据集<HDF5 dataset "conv1_W:0": shape (7, 7, 3, 64), type "<f4">>>> f['conv1']['conv1_W:0'].value # conv1_W:0 权重数据的值， 是一个标准的4d arrayarray([[[[ 2.82526277e-02, -1.18737184e-02, 1.51488732e-03, ..., -1.07003953e-02, -5.27982824e-02, -1.36667420e-03], [ 5.86827798e-03, 5.04415408e-02, 3.46324709e-03, ..., 1.01423981e-02, 1.39493728e-02, 1.67549420e-02], [-2.44090753e-03, -4.86173332e-02, 2.69966386e-03, ..., -3.44439060e-04, 3.48098315e-02, 6.28910400e-03]], [[ 1.81872323e-02, -7.20698107e-03, 4.80302610e-03, ..., …. ]]]])>>> conv1_w = np.asarray(f['conv1']['conv1_W:0']) # 直接转换成numpy格式 >>> conv1_w.shape(7, 7, 3, 64)# 卷积层>>> filtered_layers[0]<keras.layers.convolutional.Conv2D object at 0x000001F7487C0E10>>>> filtered_layers[0].name'conv1'>>> filtered_layers[0].input<tf.Tensor 'conv1_pad/Pad:0' shape=(?, 230, 230, 3) dtype=float32>#卷积层权重数据>>> filtered_layers[0].weights[<tf.Variable 'conv1/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref>, <tf.Variable 'conv1/bias:0' shape=(64,) dtype=float32_ref>]

将模型权重数据变量Tensor(tf.Variable)、读取的权重数据打包对应，便于后续将数据写入到权重变量中.

weight_value_tuples = []# 枚举过滤后的层for k, name in enumerate(layer_names): g = f[name] weight_names = load_attributes_from_hdf5_group(g, 'weight_names') # 获取文件中当前层的权重数据list， 数据类型转换为numpy array weight_values = [np.asarray(g[weight_name]) for weight_name in weight_names] # 获取keras模型中层具有的权重数据tf.Variable个数 layer = filtered_layers[k] symbolic_weights = layer.weights # 权重数据预处理 weight_values = preprocess_weights_for_loading(layer, weight_values, original_keras_version, original_backend,reshape=reshape) # 验证权重数据、tf.Variable数据是否相同 if len(weight_values) != len(symbolic_weights): raise ValueError('Layer #' + str(k) + '(named "' + layer.name + '" in the current model) was found to correspond to layer ' + name + ' in the save file. However the new layer ' + layer.name + ' expects ' + str(len(symbolic_weights)) + 'weights, but the saved weights have ' + str(len(weight_values)) + ' elements.') # tf.Variable 和 权重数据 打包 weight_value_tuples += zip(symbolic_weights, weight_values)

4、将读取的权重数据写入到层的权重变量中

在3中已经对应好每一层的权重变量Tensor和权重数据，后面将使用tensorflow的sess.run方法进新写入，后面一行代码。

K.batch_set_value(weight_value_tuples)

实际实现

def batch_set_value(tuples): if tuples: assign_ops = [] feed_dict = {} for x, value in tuples: # 获取权重数据类型 value = np.asarray(value, dtype=dtype(x)) tf_dtype = tf.as_dtype(x.dtype.name.split('_')[0]) if hasattr(x, '_assign_placeholder'): assign_placeholder = x._assign_placeholder assign_op = x._assign_op else: # 权重的tf.placeholder assign_placeholder = tf.placeholder(tf_dtype, shape=value.shape) # 对权重变量Tensor的赋值 assign的operation assign_op = x.assign(assign_placeholder) x._assign_placeholder = assign_placeholder # 用处? x._assign_op = assign_op # 用处? assign_ops.append(assign_op) feed_dict[assign_placeholder] = value # 利用tensorflow的tf.Session().run()对tensor进行assign批次赋值 get_session().run(assign_ops, feed_dict=feed_dict)

至此，先有网络模型，后从h5中加载权重文件结束。后面就可以直接利用模型进行predict了。

三、模型加载 load_model()

这里基本和前面类似，多了一个加载网络而已，后面的权重加载方式一样。

首先将前面加载权重的模型使用 model.save()保存为res50_model.h5，使用HDFView查看

属性成了3个，backend, keras_version和model_config，用于说明模型文件由某种后端生成，后端版本，以及json格式的网络模型结构。

有一个key键"model_weights", 相较于属性有前面的h5模型，属性多了2个为['backend', 'keras_version', 'layer_names'] 该key键下面的键值是一个list, 和前面的h5模型的权重数据完全一致。

类似的，先利用python代码查看下文件结构

>>> ff<HDF5 file "res50_model.h5" (mode r)>>>> ff.attrs.keys()<KeysViewHDF5 ['backend', 'keras_version', 'model_config']>>>> ff.keys()<KeysViewHDF5 ['model_weights']>>>> ff['model_weights'].attrs.keys() ## ff['model_weights']有三个属性<KeysViewHDF5 ['backend', 'keras_version', 'layer_names']>>>> ff['model_weights'].keys() ## 无顺序<KeysViewHDF5 ['activation_1', 'activation_10', 'activation_11', 'activation_12', …, 'avg_pool', 'bn2a_branch1', 'bn2a_branch2a', 'bn2a_branch2b', …, 'bn5c_branch2c', 'bn_conv1', 'conv1', 'conv1_pad', 'fc1000', 'input_1', …, 'c_branch2a', 'res5c_branch2b', 'res5c_branch2c']>>>> ff['model_weights'].attrs['layer_names'] ## 有顺序array([b'input_1', b'conv1_pad', b'conv1', b'bn_conv1', b'activation_1', b'pool1_pad', b'max_pooling2d_1', b'res2a_branch2a', b'bn2a_branch2a', b'activation_2', b'res2a_branch2b', ... 省略 b'activation_48', b'res5c_branch2c', b'bn5c_branch2c', b'add_16', b'activation_49', b'avg_pool', b'fc1000'], dtype='|S15')

1、加载模型主函数load_model

def load_model(filepath, custom_objects=None, compile=True): if h5py is None: raise ImportError('`load_model` requires h5py.') model = None opened_new_file = not isinstance(filepath, h5py.Group) # h5加载后转换为一个 h5dict 类，编译通过键取值 f = h5dict(filepath, 'r') try: # 序列化并compile model = _deserialize_model(f, custom_objects, compile) finally: if opened_new_file: f.close() return model

2、序列化并编译_deserialize_model

函数def _deserialize_model(f, custom_objects=None, compile=True)的代码显示主要部分

第一步，加载网络结构，实现完全同keras.models.model_from_json()

# 从h5中读取网络结构的json描述字符串model_config = f['model_config']model_config = json.loads(model_config.decode('utf-8'))# 根据json构建网络模型结构model = model_from_config(model_config, custom_objects=custom_objects)

第二步，加载网络权重，完全同model.load_weights()

# 获取有顺序的网络层名, 网络层model_weights_group = f['model_weights']layer_names = model_weights_group['layer_names'] layers = model.layers# 过滤 有权重Tensor的层for layer in layers: weights = layer.weights if weights: filtered_layers.append(layer)# 过滤有权重的数据filtered_layer_names = []for name in layer_names: layer_weights = model_weights_group[name] weight_names = layer_weights['weight_names'] if weight_names: filtered_layer_names.append(name)# 打包数据 weight_value_tuplesweight_value_tuples = []for k, name in enumerate(layer_names): layer_weights = model_weights_group[name] weight_names = layer_weights['weight_names'] weight_values = [layer_weights[weight_name] for weight_name in weight_names] layer = filtered_layers[k] symbolic_weights = layer.weights weight_values = preprocess_weights_for_loading(...) weight_value_tuples += zip(symbolic_weights, weight_values) # 批写入 K.batch_set_value(weight_value_tuples)

第三步，compile并返回模型

正常情况，模型网路建立、加载权重后 compile之后就完成。若还有其他设置，则可以再进行额外的处理。（模型训练后save会有额外是参数设置）。

例如，一个只有dense层的网路训练保存后查看，属性多了"training_config"，键多了"optimizer_weights"，如下图。

当前res50_model.h5没有额外的参数设置。

处理代码如下

if compile: training_config = f.get('training_config') if training_config is None: warnings.warn('No training configuration found in save file: ' 'the model was *not* compiled. Compile it manually.') return model training_config = json.loads(training_config.decode('utf-8')) optimizer_config = training_config['optimizer_config'] optimizer = optimizers.deserialize(optimizer_config, custom_objects=custom_objects) # Recover loss functions and metrics. loss = convert_custom_objects(training_config['loss']) metrics = convert_custom_objects(training_config['metrics']) sample_weight_mode = training_config['sample_weight_mode'] loss_weights = training_config['loss_weights'] # Compile model. model.compile(optimizer=optimizer, loss=loss, metrics=metrics, loss_weights=loss_weights, sample_weight_mode=sample_weight_mode) # Set optimizer weights. if 'optimizer_weights' in f: # Build train function (to get weight updates). model._make_train_function() optimizer_weights_group = f['optimizer_weights'] optimizer_weight_names = [ n.decode('utf8') for n in ptimizer_weights_group['weight_names']] optimizer_weight_values = [ optimizer_weights_group[n] for n in optimizer_weight_names] try: model.optimizer.set_weights(optimizer_weight_values) except ValueError: warnings.warn('Error in loading the saved optimizer state. As a result,' 'your model is starting with a freshly initialized optimizer.')

以上这篇keras读取h5文件load_weights、load代码操作就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。