Python 将Keras model.summary（）获取为表

我在Keras中创建了一个相当大的模型，我正在LaTeX上写一篇关于这个的文章。为了用LaTeX很好地描述keras模型，我想用它创建一个LaTeX表。我可以手工完成，但我想知道是否有更好的方法来完成这项任务

我四处寻找，发现了一些帖子，比如在哪里可以通过绘制图像来解决问题。但是，我希望将其作为文本数据（是的，有：）使用，表看起来更好，格式也更好。如果存在与此类似的内容，则为最佳选项：。但是，我找不到任何将

model.summary（）

输出转换为表格表示的方法

我在想，是否有一种方法可以将它转换成熊猫数据帧，然后使用

df.to_latex（）

处理。我曾尝试使用

model.to_json（）

，但此函数在打印

model.summary（）

时不会返回任何有关输出形状的信息。以下是我的尝试：

df = pd.DataFrame(model.to_json())
df2 = pd.DataFrame(df.loc["layers","config"])
#for example select filters, need to do it like this as it is not always contained
filters = ["-" if "filters" not in x else x["filters"] for x in df2.loc[:,"config"]]

model.to_json（）

为我的模型返回以下json：

{"class_name": "Model", "config": {"name": "Discriminator", "layers": [{"name": "input_3", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 256, 256, 1], "dtype": "float32", "sparse": false, "name": "input_3"}, "inbound_nodes": []}, {"name": "input_4", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 256, 256, 1], "dtype": "float32", "sparse": false, "name": "input_4"}, "inbound_nodes": []}, {"name": "concatenate_2", "class_name": "Concatenate", "config": {"name": "concatenate_2", "trainable": true, "dtype": "float32", "axis": -1}, "inbound_nodes": [[["input_3", 0, 0, {}], ["input_4", 0, 0, {}]]]}, {"name": "conv2d_6", "class_name": "Conv2D", "config": {"name": "conv2d_6", "trainable": true, "dtype": "float32", "filters": 8, "kernel_size": [4, 4], "strides": [2, 2], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["concatenate_2", 0, 0, {}]]]}, {"name": "leaky_re_lu_5", "class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_5", "trainable": true, "dtype": "float32", "alpha": 0.20000000298023224}, "inbound_nodes": [[["conv2d_6", 0, 0, {}]]]}, {"name": "conv2d_7", "class_name": "Conv2D", "config": {"name": "conv2d_7", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": [4, 4], "strides": [2, 2], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["leaky_re_lu_5", 0, 0, {}]]]}, {"name": "leaky_re_lu_6", "class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_6", "trainable": true, "dtype": "float32", "alpha": 0.20000000298023224}, "inbound_nodes": [[["conv2d_7", 0, 0, {}]]]}, {"name": "batch_normalization_4", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": -1, "momentum": 0.8, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["leaky_re_lu_6", 0, 0, {}]]]}, {"name": "conv2d_8", "class_name": "Conv2D", "config": {"name": "conv2d_8", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": [4, 4], "strides": [2, 2], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_4", 0, 0, {}]]]}, {"name": "leaky_re_lu_7", "class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_7", "trainable": true, "dtype": "float32", "alpha": 0.20000000298023224}, "inbound_nodes": [[["conv2d_8", 0, 0, {}]]]}, {"name": "batch_normalization_5", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": -1, "momentum": 0.8, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["leaky_re_lu_7", 0, 0, {}]]]}, {"name": "conv2d_9", "class_name": "Conv2D", "config": {"name": "conv2d_9", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [4, 4], "strides": [2, 2], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_5", 0, 0, {}]]]}, {"name": "leaky_re_lu_8", "class_name": "LeakyReLU", "config": {"name": "leaky_re_lu_8", "trainable": true, "dtype": "float32", "alpha": 0.20000000298023224}, "inbound_nodes": [[["conv2d_9", 0, 0, {}]]]}, {"name": "batch_normalization_6", "class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": -1, "momentum": 0.8, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["leaky_re_lu_8", 0, 0, {}]]]}, {"name": "conv2d_10", "class_name": "Conv2D", "config": {"name": "conv2d_10", "trainable": true, "dtype": "float32", "filters": 1, "kernel_size": [4, 4], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_6", 0, 0, {}]]]}], "input_layers": [["input_3", 0, 0], ["input_4", 0, 0]], "output_layers": [["conv2d_10", 0, 0]]}, "keras_version": "2.3.1", "backend": "tensorflow"}

虽然我想要

model.summary（）

-类似的信息：

Model: "Discriminator"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_3 (InputLayer)            (None, 256, 256, 1)  0                                            
__________________________________________________________________________________________________
input_4 (InputLayer)            (None, 256, 256, 1)  0                                            
...

---

如果我将摘要输出转换为string（）并解析字符串输出，也许有一些好方法？

因为我有更多的时间自己得到解决方案。解析模型摘要不是一个好主意。我在模型层上使用了循环，并提取了想要的信息，将其保存到数据帧中，以便以后使用。代码如下：

table=pd.DataFrame(columns=["Name","Type","Shape"])
for layer in model.layers:
    table = table.append({"Name":layer.name, "Type": layer.__class__.__name__,"Shape":layer.output_shape}, ignore_index=True)

df.head（）

的输出：

             Name         Type                  Shape
0        input_21   InputLayer  [(None, 256, 256, 1)]
1        input_22   InputLayer  [(None, 256, 256, 1)]
2  concatenate_32  Concatenate    (None, 256, 256, 2)
3       conv2d_96       Conv2D    (None, 128, 128, 8)
4  leaky_re_lu_60    LeakyReLU    (None, 128, 128, 8)