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### 記述コード
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```ここに言語を入力
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import numpy as np
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import tensorflow as tf
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import keras
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##重み（特殊な正規分布から発生する値）
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def weight(shape = []):
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initial = tf.truncated_normal(shape, stddev = 0.01)
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return tf.Variable(initial)
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##バイアス
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def bias(dtype = tf.float32, shape = []):
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initial = tf.zeros(shape, dtype = dtype)
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return tf.Variable(initial)
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##損失関数（交叉エントロピー）
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def loss(t, f):
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cross_entropy = tf.reduce_mean(-tf.reduce_sum(t * tf.log(f)))
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return cross_entropy
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##正確性の尺度
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def accuracy(t, f):
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correct_prediction = tf.equal(tf.argmax(t, 1), tf.argmax(f, 1))
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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return accuracy
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##層の数
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Q = 60
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P = 60
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R = 1
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sess = tf.InteractiveSession()
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X = tf.placeholder(dtype = tf.float32, shape = [None, Q])
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t = tf.placeholder(dtype = tf.float32, shape = [None, R])
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##隠れ層
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##活性化関数はシグモイド関数
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W1 = weight(shape = [Q, P])
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b1 = bias(shape = [P])
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f1 = tf.matmul(X, W1) + b1
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sigm = tf.nn.sigmoid(f1)
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##出力層
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##fはソフトマックス関数（出力を0～1に制限）
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W2 = weight(shape = [P, R])
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b2 = bias(shape = [R])
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f2 = tf.matmul(sigm, W2) + b2
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f = tf.nn.softmax(f2)
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loss = loss(t, f)
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acc = accuracy(t, f)
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##BP法
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optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.95)
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train_step = optimizer.minimize(loss)
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##学習を実行
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with tf.Session() as sess:
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init_op = tf.global_variables_initializer()
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sess.run(init_op)
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#ここからインデント調整
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##学習データを習得
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from sklearn.model_selection import train_test_split
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##RMSE用
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from sklearn.metrics import mean_squared_error
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from math import sqrt
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##説明変数(入力特徴量)
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x = DataFrame(input_data)
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x2 = DataFrame(input_test_data)
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##目的変数(評価データ)
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y = DataFrame(learning_output_data)
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y2 = DataFrame(learning_test_data)
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##説明変数・目的変数をそれぞれ訓練データ・テストデータに分割
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train_x = x
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test_x = x2
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train_t = y
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test_t = y2
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#データの整形(tは0.0～1.0の値に変換)
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train_x = train_x.astype(np.float)
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test_x = test_x.astype(np.float)
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train_t = train_t.T.astype(np.float)/7.0
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test_t = test_t.astype(np.float)/7.0
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