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python - Tensorflow 2.0：如何为 numpy 矩阵输入创建 feature_columns

问题描述

我了解如何在 Tensorflow 1.x 中执行此操作（链接在这里）

但是对于 Tensorflow 2.0，如何为 numpy 矩阵创建 feature_columns？

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import tensorflow as tf

X = iris['data']
y = iris['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
ds_train = tf.data.Dataset.from_tensor_slices((X_train, y_train))
ds_test = tf.data.Dataset.from_tensor_slices((X_test, y_test))

model = CustomModel(feature_columns, num_classes=y_train.shape[1])
model.compile()
model.compile('adam', loss='categorical_crossentropy', metrics='accuracy')

根据 CustomModel 的文档字符串，它要求feature_columns: The Tensorflow feature columns for the dataset.

我以 sklearn 的 iris 数据集为例。我知道 tensorflow2.0 有一个 iris 数据集。如果我使用那个数据集，我就不会有这个问题。但这不是重点。鉴于我有 numpy 矩阵，我想知道如何创建特征列以输入 tensorflow 模型。

标签： pythonnumpytensorflowtensorflow2.0

解决方案

TensorFlow文档包含我们通常需要的所有示例。如果你想创建提升树模型，还有另一篇文章

我使用这个数据集只是为了展示如何使用 Pandas 数据框创建 tf.data

import tensorflow as tf
import pandas as pd
from sklearn.model_selection import train_test_split

seaflow_train = pd.read_csv(
    "~/PycharmProjects/TensorFlow2/seaflow_21min.csv")

print(seaflow_train.head())
print(seaflow_train.columns)
seaflow_train['target'] = seaflow_train['pop']

seaflow_train = seaflow_train.drop(columns=['file_id', 'cell_id', 'time', 'd1', 'd2'])

train, test = train_test_split(seaflow_train, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)

# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
  dataframe = dataframe.copy()
  labels = dataframe.pop('target')
  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
  if shuffle:
    ds = ds.shuffle(buffer_size=len(dataframe))
  ds = ds.batch(batch_size)
  return ds

batch_size = 5 # A small batch sized is used for demonstration purposes
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)

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