質問編集履歴
2
誤字脱字の修正
test
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test
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@@ -76,10 +76,6 @@
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df2 = pd.read_csv("y_train.csv")
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79
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#df2.fillna(0)
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-
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#print(df2)
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-
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x_ = df1.iloc[:, 0:6].as_matrix()
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y_ = df2.iloc[:, 0:9].as_matrix()
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@@ -101,10 +97,6 @@
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df1 = pd.read_csv("x_test.csv")
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df2 = pd.read_csv("y_test.csv")
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#df2.fillna(0)
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#print(df2)
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x_ = df1.iloc[:, 0:6].as_matrix()
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コードの追加。
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test
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@@ -51,3 +51,123 @@
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51
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python3.6
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tensorflow1.2.1
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コードは以下の通りです。
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ただし、いろいろいじっているので他のエラーを起こすかもしれません。
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# データのインポート
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import tensorflow as tf
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import numpy as np
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import pandas as pd
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def train_read():
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df1 = pd.read_csv("x_train.csv")
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df2 = pd.read_csv("y_train.csv")
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#df2.fillna(0)
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#print(df2)
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x_ = df1.iloc[:, 0:6].as_matrix()
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y_ = df2.iloc[:, 0:9].as_matrix()
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return x_,y_
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train_x, train_y = train_read()
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print(np.shape(train_x))
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print(np.shape(train_y))
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def test_read():
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df1 = pd.read_csv("x_test.csv")
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df2 = pd.read_csv("y_test.csv")
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#df2.fillna(0)
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#print(df2)
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x_ = df1.iloc[:, 0:6].as_matrix()
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y_ = df2.iloc[:, 0:9].as_matrix()
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return x_,y_
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test_x,test_y = test_read()
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print(np.shape(test_x))
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print(np.shape(test_y))
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# モデルの作成
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x = tf.placeholder(tf.float32, [None, 6])
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w = tf.Variable(tf.zeros([6, 9]))
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b = tf.Variable(tf.zeros([9]))
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y = tf.nn.softmax(tf.matmul(x, w) + b)
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# 損失とオプティマイザーを定義
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y_ = tf.placeholder(tf.float32, [None, 9])
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cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
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train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
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# 精度
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correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
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150
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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153
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154
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# 訓練
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156
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157
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sess = tf.Session()
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158
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159
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sess.run(tf.global_variables_initializer())
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saver = tf.train.Saver()
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162
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163
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164
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165
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for i in range(101):
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166
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sess.run(train_step, feed_dict={x: train_x, y_: train_y})
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168
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if i % 10 == 0:
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170
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acc, cost = sess.run([accuracy, cross_entropy], feed_dict={x: test_x, y_: test_y})
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print('Step: %d, Accuracy: %f, Loss: %f' % (i, acc, cost))
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