Question

我正在尝试运行Keras预测函数，为此，我使用此tensorflow example作为基础，使用数据框中的“数字列”训练模型，该示例是关于二进制分类来预测是否患者患有心脏病。

我能够成功运行示例，现在我想测试预测功能。

所使用的训练数据如下（最后一列“目标”指示患者是否患有心脏病：1 = true，0 = false）：

my_columns = ["age", "sex", "cp", "trestbps", "chol", "fbs", "restecg", "thalach", "exang", "oldpeak","slope", "ca","thal", "target"]
my_data = [[63,1,4,130,254,0,2,147,0,1.4,2,1,"reversible",1]]

具有以上数据结构，准备使用预测函数的数据集的正确方法是什么？
通过使用tensorflow-example并调用预测函数来获取结果是否足以解决二进制分类问题？

预先感谢

Gher

P.S。附加Tensorflow示例的完整代码

from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
import pandas as pd

import tensorflow as tf

from tensorflow import feature_column
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split

URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
dataframe = pd.read_csv(URL)
dataframe.head()

train, test = train_test_split(dataframe, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')


# 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)

for feature_batch, label_batch in train_ds.take(1):
  print('Every feature:', list(feature_batch.keys()))
  print('A batch of ages:', feature_batch['age'])
  print('A batch of targets:', label_batch )

feature_columns = []
# numeric cols
for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:
  feature_columns.append(feature_column.numeric_column(header))

feature_layer = tf.keras.layers.DenseFeatures(feature_columns)

batch_size = 32
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)


model = tf.keras.Sequential([
  feature_layer,
  layers.Dense(128, activation='relu'),
  layers.Dense(128, activation='relu'),
  layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])
model.fit(train_ds,
          validation_data=val_ds,
          epochs=5)
loss, accuracy = model.evaluate(test_ds)
print("Accuracy", accuracy)

Keras模型预测函数

0 个答案: