前面两个练习都是用TensorFlow Core写的,相对于数据流图的概念比较清晰。TensorFlow本身也封装了很多高层API,方面我们做开发方便。很多模型和函数都有(东西太多了完全看不完啊)。
因为是封装后的模型,就不怎么逐步解释了。主要是process-fit-evaluate-predict过程。
直接贴代码 本来是跟着书学习的,奈何现在的是API是1.1。0.6的教程有太多的不一样。
下面贴的是TensorFlow1.1的高层API练习。一个分类一个回归。
植物种类的分类
# -*- coding: UTF-8 -*
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import urllib
import numpy as np
import tensorflow as tf
# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv"
IRIS_TEST = "iris_test.csv"
IRIS_TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
def main():
# If the training and test sets aren't stored locally, download them.
if not os.path.exists(IRIS_TRAINING):
raw = urllib.urlopen(IRIS_TRAINING_URL).read()
with open(IRIS_TRAINING, "w") as f:
f.write(raw)
if not os.path.exists(IRIS_TEST):
raw = urllib.urlopen(IRIS_TEST_URL).read()
with open(IRIS_TEST, "w") as f:
f.write(raw)
# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=IRIS_TRAINING,
target_dtype=np.int,
features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=IRIS_TEST,
target_dtype=np.int,
features_dtype=np.float32)
# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
n_classes=3,
model_dir="/tmp/iris_model")
# Define the training inputs
def get_train_inputs():
x = tf.constant(training_set.data)
y = tf.constant(training_set.target)
return x, y
# Fit model.
classifier.fit(input_fn=get_train_inputs, steps=2000)
# Define the test inputs
def get_test_inputs():
x = tf.constant(test_set.data)
y = tf.constant(test_set.target)
return x, y
# Evaluate accuracy.
accuracy_score = classifier.evaluate(input_fn=get_test_inputs,
steps=1)["accuracy"]
print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
# Classify two new flower samples.
def new_samples():
return np.array(
[[6.4, 3.2, 4.5, 1.5],
[5.8, 3.1, 5.0, 1.7]], dtype=np.float32)
predictions = list(classifier.predict(input_fn=new_samples))
print(
"New Samples, Class Predictions: {}\n"
.format(predictions))
if __name__ == "__main__":
main()
波士顿房价回归
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import pandas as pd
import tensorflow as tf
import warnings
warnings.filterwarnings("ignore")
tf.logging.set_verbosity(tf.logging.INFO)
COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age",
"dis", "tax", "ptratio", "medv"]
FEATURES = ["crim", "zn", "indus", "nox", "rm",
"age", "dis", "tax", "ptratio"]
LABEL = "medv"
training_set = pd.read_csv("../data/boston_train.csv", skipinitialspace=True,
skiprows=1, names=COLUMNS)
test_set = pd.read_csv("../data/boston_test.csv", skipinitialspace=True,
skiprows=1, names=COLUMNS)
prediction_set = pd.read_csv("../data/boston_predict.csv", skipinitialspace=True,
skiprows=1, names=COLUMNS)
feature_cols = [tf.contrib.layers.real_valued_column(k)
for k in FEATURES]
regressor = tf.contrib.learn.DNNRegressor(feature_columns=feature_cols,
hidden_units=[10, 10],
model_dir="/tmp/boston_model")
def input_fn(data_set):
feature_cols = {k: tf.constant(data_set[k].values)
for k in FEATURES}
labels = tf.constant(data_set[LABEL].values)
return feature_cols, labels
#fit
regressor.fit(input_fn=lambda: input_fn(training_set), steps=5000)
#evaluate
ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1)
loss_score = ev["loss"]
print("Loss: {0:f}".format(loss_score))
#predict
y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
# .predict() returns an iterator; convert to a list and print predictions
predictions = list(itertools.islice(y, 6))
print ("Predictions: {}".format(str(predictions)))