質問編集履歴
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コードブロックの使用
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pythonで機械学習のプログラムを作るうえで
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一変数ではなく、多変数のデータを読み込んで実行したい
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一変数でのプログラムは作って実行もできたので、そこに何かを付け足せばいいのか
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または新しく作り直さなければならないのかを知りたく、またそうするにはどうすればいいかを教えてほしいです
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### 該当のソースコード
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```python
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import tensorflow as tf
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import numpy as np
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import random
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num_of_input_nodes = 1
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num_of_hidden_nodes = 80
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num_of_output_nodes = 1
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length_of_sequences = 10 ← 学習・識別時の時系列データ数（area.txtとarea2.txtの列数）
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num_of_training_epochs = 5000 ← 学習回数
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size_of_mini_batch = 10
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num_of_prediction_epochs = 107 ← 識別ファイル（area2.txtとseikai2.txtの行数）
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learning_rate = 0.01
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forget_bias = 0.8
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num_of_sample = 109 ← 学習ファイル（area.txtとseikai.txtの行数）
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def get_batch(batch_size, X, t):
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rnum = [random.randint(0, len(X) - 1) for x in range(batch_size)]
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xs = np.array([[[y] for y in list(X[r])] for r in rnum])
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ts = np.array([[t[r]] for r in rnum])
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return xs, ts
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def create_data(nb_of_samples, sequence_len):
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X = np.loadtxt('area.txt') ← 学習データファイルの読込み
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t = np.loadtxt('seikai.txt') ← 学習正解値ファイルの読込み
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print(X)
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print(t)
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return X, t
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def make_prediction(nb_of_samples):
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xs = np.loadtxt('area2.txt') ← 識別データファイルの読込み
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ts = np.loadtxt('seikai2.txt') ← 識別正解値データファイルの読込み（正解率計算用。実際の使用時は正解値は使用しない）
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return np.array([[[y] for y in x] for x in xs]), np.array([[x] for x in ts])
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def inference(input_ph, istate_ph):
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with tf.name_scope("inference") as scope:
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weight1_var = tf.Variable(tf.truncated_normal(
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[num_of_input_nodes, num_of_hidden_nodes], stddev=0.1), name="weight1")
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weight2_var = tf.Variable(tf.truncated_normal(
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[num_of_hidden_nodes, num_of_output_nodes], stddev=0.1), name="weight2")
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bias1_var = tf.Variable(tf.truncated_normal([num_of_hidden_nodes], stddev=0.1), name="bias1")
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bias2_var = tf.Variable(tf.truncated_normal([num_of_output_nodes], stddev=0.1), name="bias2")
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in1 = tf.transpose(input_ph, [1, 0, 2])
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in2 = tf.reshape(in1, [-1, num_of_input_nodes])
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in3 = tf.matmul(in2, weight1_var) + bias1_var
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in4 = tf.split(in3, length_of_sequences, 0)
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cell = tf.nn.rnn_cell.BasicLSTMCell(num_of_hidden_nodes, forget_bias=forget_bias, state_is_tuple=False)
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rnn_output, states_op = tf.contrib.rnn.static_rnn(cell, in4, initial_state=istate_ph)
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output_op = tf.matmul(rnn_output[-1], weight2_var) + bias2_var
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# Add summary ops to collect data
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w1_hist = tf.summary.histogram("weights1", weight1_var)
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w2_hist = tf.summary.histogram("weights2", weight2_var)
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b1_hist = tf.summary.histogram("biases1", bias1_var)
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b2_hist = tf.summary.histogram("biases2", bias2_var)
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output_hist = tf.summary.histogram("output", output_op)
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results = [weight1_var, weight2_var, bias1_var, bias2_var]
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return output_op, states_op, results
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def loss(output_op, supervisor_ph):
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with tf.name_scope("loss") as scope:
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square_error = tf.reduce_mean(tf.square(output_op - supervisor_ph))
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loss_op = square_error
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tf.summary.scalar("loss", loss_op)
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return loss_op
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def training(loss_op):
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with tf.name_scope("training") as scope:
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training_op = optimizer.minimize(loss_op)
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return training_op
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def calc_accuracy(output_op, prints=False):
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inputs, ts = make_prediction(num_of_prediction_epochs)
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pred_dict = {
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input_ph: inputs,
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supervisor_ph: ts,
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istate_ph: np.zeros((num_of_prediction_epochs, num_of_hidden_nodes * 2)),
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}
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output = sess.run([output_op], feed_dict=pred_dict)
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def print_result(i, p, q):
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[print(list(x)[0]) for x in i]
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print("output: %f, correct: %d" % (p, q))
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if prints:
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[print_result(i, p, q) for i, p, q in zip(inputs, output[0], ts)]
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opt = abs(output - ts)[0]
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total = sum([1 if x[0] < 0.5 else 0 for x in opt]) ← 0.05から0.5に変更
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print("accuracy %f" % (total / float(len(ts))))
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return output
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random.seed(0)
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np.random.seed(0)
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tf.set_random_seed(0)
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optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
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X, t = create_data(num_of_sample, length_of_sequences)
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with tf.Graph().as_default():
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input_ph = tf.placeholder(tf.float32, [None, length_of_sequences, num_of_input_nodes], name="input")
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supervisor_ph = tf.placeholder(tf.float32, [None, num_of_output_nodes], name="supervisor")
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istate_ph = tf.placeholder(tf.float32, [None, num_of_hidden_nodes * 2], name="istate")
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output_op, states_op, datas_op = inference(input_ph, istate_ph)
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loss_op = loss(output_op, supervisor_ph)
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training_op = training(loss_op)
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summary_op = tf.summary.merge_all()
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init = tf.initialize_all_variables()
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with tf.Session() as sess:
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saver = tf.train.Saver()
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summary_writer = tf.summary.FileWriter("/tmp/tensorflow_log", graph=sess.graph)
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sess.run(init)
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for epoch in range(num_of_training_epochs):
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inputs, supervisors = get_batch(size_of_mini_batch, X, t)
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train_dict = {
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input_ph: inputs,
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supervisor_ph: supervisors,
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istate_ph: np.zeros((size_of_mini_batch, num_of_hidden_nodes * 2)),
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}
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sess.run(training_op, feed_dict=train_dict)
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if (epoch) % 100 == 0:
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summary_str, train_loss = sess.run([summary_op, loss_op], feed_dict=train_dict)
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print("train#%d, train loss: %e" % (epoch, train_loss))
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summary_writer.add_summary(summary_str, epoch)
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if (epoch) % 500 == 0:
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calc_accuracy(output_op)
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calc_accuracy(output_op, prints=True)
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datas = sess.run(datas_op)
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saver.save(sess, "model.ckpt")
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```
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7
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python
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8
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-
|
|
9
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-
```で機械学習のプログラムを作るうえで
|
|
10
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-
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11
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一変数ではなく、多変数のデータを読み込んで実行したい
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12
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-
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13
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14
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15
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-
一変数でのプログラムは作って実行もできたので、そこに何かを付け足せばいいのか
|
|
16
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-
|
|
17
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または新しく作り直さなければならないのかを知りたく、またそうするにはどうすればいいかを教えてほしいです
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### 該当のソースコード
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24
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import tensorflow as tf
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import numpy as np
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import random
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num_of_input_nodes = 1
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num_of_hidden_nodes = 80
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44
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45
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num_of_output_nodes = 1
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46
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-
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47
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length_of_sequences = 10 ← 学習・識別時の時系列データ数（area.txtとarea2.txtの列数）
|
|
48
|
-
|
|
49
|
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num_of_training_epochs = 5000 ← 学習回数
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50
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-
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|
51
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size_of_mini_batch = 10
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|
52
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-
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53
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num_of_prediction_epochs = 107 ← 識別ファイル（area2.txtとseikai2.txtの行数）
|
|
54
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-
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55
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learning_rate = 0.01
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56
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-
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57
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-
forget_bias = 0.8
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58
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-
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59
|
-
num_of_sample = 109 ← 学習ファイル（area.txtとseikai.txtの行数）
|
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60
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-
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61
|
-
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62
|
-
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63
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-
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64
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-
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|
65
|
-
def get_batch(batch_size, X, t):
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66
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-
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67
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-
rnum = [random.randint(0, len(X) - 1) for x in range(batch_size)]
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68
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69
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-
xs = np.array([[[y] for y in list(X[r])] for r in rnum])
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70
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-
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71
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-
ts = np.array([[t[r]] for r in rnum])
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72
|
-
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|
73
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-
return xs, ts
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74
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-
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|
75
|
-
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76
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-
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77
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-
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|
78
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-
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79
|
-
def create_data(nb_of_samples, sequence_len):
|
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80
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-
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81
|
-
X = np.loadtxt('area.txt') ← 学習データファイルの読込み
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82
|
-
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83
|
-
t = np.loadtxt('seikai.txt') ← 学習正解値ファイルの読込み
|
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84
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-
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|
85
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-
print(X)
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86
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87
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-
print(t)
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88
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-
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89
|
-
return X, t
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|
90
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-
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91
|
-
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|
92
|
-
|
|
93
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-
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94
|
-
|
|
95
|
-
def make_prediction(nb_of_samples):
|
|
96
|
-
|
|
97
|
-
xs = np.loadtxt('area2.txt') ← 識別データファイルの読込み
|
|
98
|
-
|
|
99
|
-
ts = np.loadtxt('seikai2.txt') ← 識別正解値データファイルの読込み（正解率計算用。実際の使用時は正解値は使用しない）
|
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100
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-
|
|
101
|
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return np.array([[[y] for y in x] for x in xs]), np.array([[x] for x in ts])
|
|
102
|
-
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103
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-
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|
104
|
-
|
|
105
|
-
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106
|
-
|
|
107
|
-
def inference(input_ph, istate_ph):
|
|
108
|
-
|
|
109
|
-
with tf.name_scope("inference") as scope:
|
|
110
|
-
|
|
111
|
-
weight1_var = tf.Variable(tf.truncated_normal(
|
|
112
|
-
|
|
113
|
-
[num_of_input_nodes, num_of_hidden_nodes], stddev=0.1), name="weight1")
|
|
114
|
-
|
|
115
|
-
weight2_var = tf.Variable(tf.truncated_normal(
|
|
116
|
-
|
|
117
|
-
[num_of_hidden_nodes, num_of_output_nodes], stddev=0.1), name="weight2")
|
|
118
|
-
|
|
119
|
-
bias1_var = tf.Variable(tf.truncated_normal([num_of_hidden_nodes], stddev=0.1), name="bias1")
|
|
120
|
-
|
|
121
|
-
bias2_var = tf.Variable(tf.truncated_normal([num_of_output_nodes], stddev=0.1), name="bias2")
|
|
122
|
-
|
|
123
|
-
|
|
124
|
-
|
|
125
|
-
in1 = tf.transpose(input_ph, [1, 0, 2])
|
|
126
|
-
|
|
127
|
-
in2 = tf.reshape(in1, [-1, num_of_input_nodes])
|
|
128
|
-
|
|
129
|
-
in3 = tf.matmul(in2, weight1_var) + bias1_var
|
|
130
|
-
|
|
131
|
-
in4 = tf.split(in3, length_of_sequences, 0)
|
|
132
|
-
|
|
133
|
-
|
|
134
|
-
|
|
135
|
-
cell = tf.nn.rnn_cell.BasicLSTMCell(num_of_hidden_nodes, forget_bias=forget_bias, state_is_tuple=False)
|
|
136
|
-
|
|
137
|
-
rnn_output, states_op = tf.contrib.rnn.static_rnn(cell, in4, initial_state=istate_ph)
|
|
138
|
-
|
|
139
|
-
output_op = tf.matmul(rnn_output[-1], weight2_var) + bias2_var
|
|
140
|
-
|
|
141
|
-
|
|
142
|
-
|
|
143
|
-
# Add summary ops to collect data
|
|
144
|
-
|
|
145
|
-
w1_hist = tf.summary.histogram("weights1", weight1_var)
|
|
146
|
-
|
|
147
|
-
w2_hist = tf.summary.histogram("weights2", weight2_var)
|
|
148
|
-
|
|
149
|
-
b1_hist = tf.summary.histogram("biases1", bias1_var)
|
|
150
|
-
|
|
151
|
-
b2_hist = tf.summary.histogram("biases2", bias2_var)
|
|
152
|
-
|
|
153
|
-
output_hist = tf.summary.histogram("output", output_op)
|
|
154
|
-
|
|
155
|
-
results = [weight1_var, weight2_var, bias1_var, bias2_var]
|
|
156
|
-
|
|
157
|
-
return output_op, states_op, results
|
|
158
|
-
|
|
159
|
-
|
|
160
|
-
|
|
161
|
-
|
|
162
|
-
|
|
163
|
-
def loss(output_op, supervisor_ph):
|
|
164
|
-
|
|
165
|
-
with tf.name_scope("loss") as scope:
|
|
166
|
-
|
|
167
|
-
square_error = tf.reduce_mean(tf.square(output_op - supervisor_ph))
|
|
168
|
-
|
|
169
|
-
loss_op = square_error
|
|
170
|
-
|
|
171
|
-
tf.summary.scalar("loss", loss_op)
|
|
172
|
-
|
|
173
|
-
return loss_op
|
|
174
|
-
|
|
175
|
-
|
|
176
|
-
|
|
177
|
-
|
|
178
|
-
|
|
179
|
-
def training(loss_op):
|
|
180
|
-
|
|
181
|
-
with tf.name_scope("training") as scope:
|
|
182
|
-
|
|
183
|
-
training_op = optimizer.minimize(loss_op)
|
|
184
|
-
|
|
185
|
-
return training_op
|
|
186
|
-
|
|
187
|
-
|
|
188
|
-
|
|
189
|
-
|
|
190
|
-
|
|
191
|
-
def calc_accuracy(output_op, prints=False):
|
|
192
|
-
|
|
193
|
-
inputs, ts = make_prediction(num_of_prediction_epochs)
|
|
194
|
-
|
|
195
|
-
pred_dict = {
|
|
196
|
-
|
|
197
|
-
input_ph: inputs,
|
|
198
|
-
|
|
199
|
-
supervisor_ph: ts,
|
|
200
|
-
|
|
201
|
-
istate_ph: np.zeros((num_of_prediction_epochs, num_of_hidden_nodes * 2)),
|
|
202
|
-
|
|
203
|
-
}
|
|
204
|
-
|
|
205
|
-
output = sess.run([output_op], feed_dict=pred_dict)
|
|
206
|
-
|
|
207
|
-
|
|
208
|
-
|
|
209
|
-
def print_result(i, p, q):
|
|
210
|
-
|
|
211
|
-
[print(list(x)[0]) for x in i]
|
|
212
|
-
|
|
213
|
-
print("output: %f, correct: %d" % (p, q))
|
|
214
|
-
|
|
215
|
-
if prints:
|
|
216
|
-
|
|
217
|
-
[print_result(i, p, q) for i, p, q in zip(inputs, output[0], ts)]
|
|
218
|
-
|
|
219
|
-
|
|
220
|
-
|
|
221
|
-
opt = abs(output - ts)[0]
|
|
222
|
-
|
|
223
|
-
total = sum([1 if x[0] < 0.5 else 0 for x in opt]) ← 0.05から0.5に変更
|
|
224
|
-
|
|
225
|
-
print("accuracy %f" % (total / float(len(ts))))
|
|
226
|
-
|
|
227
|
-
return output
|
|
228
|
-
|
|
229
|
-
|
|
230
|
-
|
|
231
|
-
random.seed(0)
|
|
232
|
-
|
|
233
|
-
np.random.seed(0)
|
|
234
|
-
|
|
235
|
-
tf.set_random_seed(0)
|
|
236
|
-
|
|
237
|
-
|
|
238
|
-
|
|
239
|
-
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
|
|
240
|
-
|
|
241
|
-
|
|
242
|
-
|
|
243
|
-
X, t = create_data(num_of_sample, length_of_sequences)
|
|
244
|
-
|
|
245
|
-
|
|
246
|
-
|
|
247
|
-
with tf.Graph().as_default():
|
|
248
|
-
|
|
249
|
-
input_ph = tf.placeholder(tf.float32, [None, length_of_sequences, num_of_input_nodes], name="input")
|
|
250
|
-
|
|
251
|
-
supervisor_ph = tf.placeholder(tf.float32, [None, num_of_output_nodes], name="supervisor")
|
|
252
|
-
|
|
253
|
-
istate_ph = tf.placeholder(tf.float32, [None, num_of_hidden_nodes * 2], name="istate")
|
|
254
|
-
|
|
255
|
-
|
|
256
|
-
|
|
257
|
-
output_op, states_op, datas_op = inference(input_ph, istate_ph)
|
|
258
|
-
|
|
259
|
-
loss_op = loss(output_op, supervisor_ph)
|
|
260
|
-
|
|
261
|
-
training_op = training(loss_op)
|
|
262
|
-
|
|
263
|
-
|
|
264
|
-
|
|
265
|
-
summary_op = tf.summary.merge_all()
|
|
266
|
-
|
|
267
|
-
init = tf.initialize_all_variables()
|
|
268
|
-
|
|
269
|
-
|
|
270
|
-
|
|
271
|
-
with tf.Session() as sess:
|
|
272
|
-
|
|
273
|
-
saver = tf.train.Saver()
|
|
274
|
-
|
|
275
|
-
summary_writer = tf.summary.FileWriter("/tmp/tensorflow_log", graph=sess.graph)
|
|
276
|
-
|
|
277
|
-
sess.run(init)
|
|
278
|
-
|
|
279
|
-
|
|
280
|
-
|
|
281
|
-
for epoch in range(num_of_training_epochs):
|
|
282
|
-
|
|
283
|
-
inputs, supervisors = get_batch(size_of_mini_batch, X, t)
|
|
284
|
-
|
|
285
|
-
train_dict = {
|
|
286
|
-
|
|
287
|
-
input_ph: inputs,
|
|
288
|
-
|
|
289
|
-
supervisor_ph: supervisors,
|
|
290
|
-
|
|
291
|
-
istate_ph: np.zeros((size_of_mini_batch, num_of_hidden_nodes * 2)),
|
|
292
|
-
|
|
293
|
-
}
|
|
294
|
-
|
|
295
|
-
sess.run(training_op, feed_dict=train_dict)
|
|
296
|
-
|
|
297
|
-
|
|
298
|
-
|
|
299
|
-
if (epoch) % 100 == 0:
|
|
300
|
-
|
|
301
|
-
summary_str, train_loss = sess.run([summary_op, loss_op], feed_dict=train_dict)
|
|
302
|
-
|
|
303
|
-
print("train#%d, train loss: %e" % (epoch, train_loss))
|
|
304
|
-
|
|
305
|
-
summary_writer.add_summary(summary_str, epoch)
|
|
306
|
-
|
|
307
|
-
if (epoch) % 500 == 0:
|
|
308
|
-
|
|
309
|
-
calc_accuracy(output_op)
|
|
310
|
-
|
|
311
|
-
|
|
312
|
-
|
|
313
|
-
calc_accuracy(output_op, prints=True)
|
|
314
|
-
|
|
315
|
-
datas = sess.run(datas_op)
|
|
316
|
-
|
|
317
|
-
saver.save(sess, "model.ckpt")
|
1
コードブロックの使用
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -2,7 +2,11 @@
|
|
|
2
2
|
|
|
3
3
|
|
|
4
4
|
|
|
5
|
+
```
|
|
6
|
+
|
|
7
|
+
python
|
|
8
|
+
|
|
5
|
-
|
|
9
|
+
```で機械学習のプログラムを作るうえで
|
|
6
10
|
|
|
7
11
|
一変数ではなく、多変数のデータを読み込んで実行したい
|
|
8
12
|
|