我在 Keras 中创建了相当大的模型,我正在用 LaTeX 写一篇关于它的文章。为了很好地描述 LaTeX 中的 keras 模型,我想用它创建一个 LaTeX 表。我可以手动实现它,但我想知道是否有任何“更好”的方法来实现这一点。
我四处寻找,发现了一些帖子,例如Keras model.summary( ) 有一个不错的输出吗? https://stackoverflow.com/questions/56360776/is-there-a-nice-output-of-keras-model-summary通过绘制图像来解决。然而我想把它作为文本数据(是的,拥有MRE https://stackoverflow.com/help/minimal-reproducible-example:) ),表格看起来更好并且格式也更好。如果有类似的事情,最好的选择是:statsmodels 总结到 Latex https://stackoverflow.com/questions/30011618/statsmodels-summary-to-latex。但是我无法找到任何转换方法model.summary()
输出到表格表示。
我在想是否有一种方法可以将其转换为 pandas 数据帧,然后可以使用df.to_latex()
。我尝试过这样做model.to_json()
,但此函数不会返回有关输出形状的任何信息model.summary()
印刷。这是我的尝试:
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"]]
The 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字符串,也许有一些好方法(Keras model.summary() 对象转字符串 https://stackoverflow.com/q/41665799/3157428)并解析字符串输出?