Tensorflow

Earlier when you trained for extra epochs you had an issue where your loss might change. It might have taken a bit of time for you to wait for the training to do that, and you might have thought ‘wouldn’t it be nice if I could stop the training when I reach a desired value?’ – i.e. 95% accuracy might be enough for you, and if you reach that after 3 epochs, why sit around waiting for it to finish a lot more epochs….

線形回帰は　$Ax= b$ の形でメトリックス方程式で表すことができる。ここで係数$x$を求めたい。 結果は $x = (A^T A)^{-1}A^Tb$ 二次元のデータを生成してTensorflowを利用して、問題を解決する。 以下ではA.shape = (100, 2), b.shape=(100,1) 最後にx.shape=(2,1)、xの中の要素はslope(傾き)とy intercept(y切片) import matplotlib.pyplot as plt import numpy as np import tensorflow as tf sess = tf.Session() x_vals = np.linspace(0, 10, 100) # 標準正規乱数 (平均:0.0, 標準偏差:1.0) に従う乱数を 100 件出力 y_vals = x_vals + np.random.normal(0, 1, 100) x_vals_column = np.transpose(np.matrix(x_vals)) ones_column = np.transpose(np.matrix(np.repeat(1, 100))) A = np.column_stack((x_vals_column, ones_column)) b = np.transpose(np.matrix(y_vals)) A_tensor = tf.constant(A) b_tensor = tf.