import requests
import numpy as np
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r = requests.get('')
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with open('iris.data', 'w') as f:
f.write(r.text)
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import pandas as pd
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data = pd.read_csv('iris.data', names =['e_cd', 'e_kd', 'b_cd', 'b_kd', 'cat'])
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data.head(5)
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e_cd
e_kd
b_cd
b_kd
cat0
5.13.5
1.40.2
iris-setosa
14.9
3.01.4
0.2iris-setosa
24.7
3.21.3
0.2iris-setosa
34.6
3.11.5
0.2iris-setosa
45.0
3.61.4
0.2iris-setosa
data.cat.unique()
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array(['iris-setosa', 'iris-versicolor', 'iris-virginica'], dtype=object)
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data['c1'] = np.array(data['cat'] == 'iris-setosa').astype(np.float32)
data['c2'] = np.array(data['cat'] == 'iris-versicolor').astype(np.float32)
data['c3'] = np.array(data['cat'] == 'iris-virginica').astype(np.float32)
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target = np.stack([data.c1.values, data.c2.values, data.c3.values]).t
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shuju = np.stack([data.e_cd.values, data.e_kd.values, data.b_cd.values, data.b_kd.values]).t
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np.shape(shuju), np.shape(target)
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((150, 4), (150, 3))
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import tensorflow as tf
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/anaconda3/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: runtimewarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5
return f(*args, **kwds)
/anaconda3/envs/py35/lib/python3.5/site-packages/h5py/__init__.py:36: futurewarning: conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. in future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
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x = tf.placeholder("float", shape=[none, 4])
y = tf.placeholder("float", shape=[none, 3])
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weight = tf.variable(tf.truncated_normal([4,3]))
bias = tf.variable(tf.truncated_normal([3]))
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combine_input = tf.matmul(x, weight) + bias
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pred = tf.nn.softmax(combine_input)
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y.get_shape(), pred.get_shape()
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(tensorshape([dimension(none), dimension(3)]),
tensorshape([dimension(none), dimension(3)]))
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loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=combine_input))
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warning:tensorflow:from :1: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
instructions for updating:
future major versions of tensorflow will allow gradients to flow
into the labels input on backprop by default.
see tf.nn.softmax_cross_entropy_with_logits_v2.
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correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
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train_step = tf.train.adamoptimizer(0.0005).minimize(loss)
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sess = tf.session()
sess.run(tf.global_variables_initializer())
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for i in range(10000):
index = np.random.permutation(len(target))
shuju =shuju[index]
target = target[index]
sess.run(train_step, feed_dict=)
if i%1000 == 0:
print(sess.run((loss, accuracy), feed_dict=))
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(0.48896936, 0.74)
(0.3105031, 0.91333336)
(0.23715515, 0.9533333)
(0.18639529, 0.96666664)
(0.15062416, 0.96666664)
(0.12501644, 0.98)
(0.10630642, 0.98)
(0.09238311, 0.98)
(0.08186688, 0.98)
(0.07382768, 0.98)
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print(sess.run(weight))
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[[ 1.319767 1.5383282 -1.6495701 ]
[ 4.432047 0.22605467 -3.0211918 ]
[-4.785147 -1.7326641 3.8946218 ]
[-4.521448 -2.30758 3.069917 ]]
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print(sess.run(bias))
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[ 1.521937 2.874068 -4.503109]
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