Iris CSVデータからトレーニング用のTensorflowを完全に使用する

Question

Tensorflowを使用して分類のための神経ネットワークモデルを訓練し、IrisデータセットなどのCSVファイルからデータを読み取ろうとしています。

Tensorflow documentationは、Irisデータを読み込んで予測モデルを構築する例を示していますが、この例では上位レベルのtf.contrib.learn APIが使用されています。私は、低レベルのTensorflow APIを使用して、グラデーションの下降を自分自身で実行したいと思います。どうすればいい？

Answer 1

以下は、CSVファイルからアイリスデータをロードし、2層ニューラルネットワークをトレーニングするための完全なスクリプトです。 CSVファイル（トレーニングとテストセット）はTensorflow example pageからダウンロードできますが、最初に各ファイルからヘッダー行を削除する必要があります。以下は

#!/usr/bin/env python 
""" 
Example Tensorflow code to train a 2-layer (input, hidden, output) 
neural network machine learning model using the Iris data set. At the end, 
the script computes the prediction accuracy for both the training and 
test sets. 
""" 

import tensorflow as tf 
import numpy as np 
import time 

# Data sets for Iris. Neither file should have headers, so the training file 
# should have 120 rows, and the test file should have 30 rows. 
IRIS_TRAINING = "iris_training.csv" 
IRIS_TEST = "iris_test.csv" 

# Pass this configuration to tf.Session() to disable GPU. 
CONFIG_CPU_ONLY = tf.ConfigProto(device_count = {'GPU' : 0}) 

class Config: 
    learning_rate = 0.01   # Gradient descent learning rate. 
    num_epochs = 10000    # Gradient descent number of iterations. 
    H1_size = 10     # Size of 1st (and only) hidden layer. 
    regularization_strength = 0.1 # Regularization. 

class Model: 
    """ 
    Parameters for 2-layer NN model (input, hidden, output) learned 
    during training. 
    """ 
    W1 = None 
    b1 = None 
    W2 = None 
    b2 = None 

class Data: 
    """ 
    Utility class for loading training and test CSV files. 
    """ 
    def __init__(self): 
     self.training_features = None 
     self.training_labels = None 
     self.training_labels_1hot = None 
     self.test_features = None 
     self.test_labels = None 
     self.test_labels_1hot = None 

    def load(self, training_filename, test_filename): 
     """ 
     Load CSV files into class member variables. 
     """ 

     # Load training data using load_csv() function from Tensorflow 0.10. 
     training_set = tf.contrib.learn.datasets.base.load_csv(
      filename=training_filename, target_dtype=np.int, has_header=False) 

     self.training_features = training_set.data.astype(np.float32) 
     self.training_labels  = training_set.target 
     self.training_labels_1hot = self.convert_to_one_hot(self.training_labels) 

     # Load test data using load_csv() function from Tensorflow 0.10. 
     test_set = tf.contrib.learn.datasets.base.load_csv(
      filename=test_filename, target_dtype=np.int, has_header=False) 

     self.test_features = test_set.data.astype(np.float32) 
     self.test_labels  = test_set.target 
     self.test_labels_1hot = self.convert_to_one_hot(self.test_labels) 

    def convert_to_one_hot(self, vector, num_classes=None): 
     """ 
     Converts an input 1-D vector of integers into an output 
     2-D array of one-hot vectors, where an i'th input value 
     of j will set a '1' in the i'th row, j'th column of the 
     output array. 

     Example: 
      v = np.array((1, 0, 4)) 
      one_hot_v = convert_to_one_hot(v) 
      print one_hot_v 

      [[0 1 0 0 0] 
      [1 0 0 0 0] 
      [0 0 0 0 1]] 
     """ 
     assert isinstance(vector, np.ndarray) 
     assert len(vector) > 0 
     if num_classes is None: 
      num_classes = np.max(vector)+1 
     else: 
      assert num_classes > 0 
      assert num_classes >= np.max(vector) 
     result = np.zeros(shape=(len(vector), num_classes)) 
     result[np.arange(len(vector)), vector] = 1 
     return result.astype(int) 

class IrisClassifier: 
    """ 
    Trains a 2-layer neural network model for classifying the Iris data set. 
    """ 

    def __init__(self): 
     self.data = None 

    def loadData(self): 
     """ 
     Load data from CSV files. 
     """ 
     self.data = Data() 
     self.data.load(IRIS_TRAINING, IRIS_TEST) 

    def trainModel(self): 
     """ 
     Trains a 2-layer NN model using TensorFlow. 
     Layers: Input --> Hidden --> Output 
     """ 
     num_features = self.data.training_features.shape[1] 
     num_classes = self.data.training_labels_1hot.shape[1] 

     # Create placeholders for the training data. Note that the 
     # number of rows is set to None so that different size data sets 
     # (or batches) can be loaded. 
     x_ph = tf.placeholder(tf.float32, [None, num_features]) 
     y_ph = tf.placeholder(tf.float32, [None, num_classes]) 

     # Construct hidden layer. 
     W1 = tf.get_variable(name="W1", 
          shape=[num_features, Config.H1_size], 
          initializer=tf.contrib.layers.xavier_initializer()) 
     b1 = tf.get_variable(name="b1", 
          shape=[Config.H1_size], 
          initializer=tf.constant_initializer(0.0)) 
     H1 = tf.matmul(x_ph, W1) + b1 
     H1 = tf.nn.relu(H1) 

     # Construct output layer. 
     W2 = tf.get_variable(name="W2", 
          shape=[Config.H1_size, num_classes], 
          initializer=tf.contrib.layers.xavier_initializer()) 
     b2 = tf.get_variable(name="b2", 
          shape=[num_classes], 
          initializer=tf.constant_initializer(0.0)) 
     y_hat = tf.matmul(H1, W2) + b2 

     # Loss function. Computes cross-entropy loss between computed y_hat 
     # and y_ph (which holds true values). The y_hat values are normalized 
     # with softmax. 
     J = tf.reduce_mean(
       tf.nn.softmax_cross_entropy_with_logits(y_hat, y_ph) + \ 
       Config.regularization_strength * tf.nn.l2_loss(W1) + \ 
       Config.regularization_strength * tf.nn.l2_loss(W2)) 

     train_step = tf.train.GradientDescentOptimizer(Config.learning_rate).minimize(J) 

     sess = tf.Session(config=CONFIG_CPU_ONLY) 
     sess.run(tf.initialize_all_variables()) 

     start_time = time.time() 

     # --- Gradient descent loop. ----------------------------------------- 
     for i in range(Config.num_epochs): 
      op, J_result = sess.run([train_step, J], feed_dict={x_ph:self.data.training_features, y_ph: self.data.training_labels_1hot}) 

      if (i % 1000 == 0): 
       print "Epoch %6d/%6d: J=%10.5f" % (i, Config.num_epochs, J_result) 
     # -------------------------------------------------------------------- 

     end_time = time.time() 

     total_time_in_seconds = end_time-start_time 
     print "Training took %.2f seconds" % total_time_in_seconds 

     # Save the model parameters in case you need it. 
     model = Model() 
     model.W1, model.b1, model.W2, model.b2 = sess.run([W1, b1, W2, b2]) 

     # Compute accuracy on training set. 
     correct_predictions_op = tf.equal(tf.argmax(y_hat, 1), tf.argmax(y_ph, 1)) # List of T,F 
     accuracy_op = tf.reduce_mean(tf.cast(correct_predictions_op, tf.float32)) 
     correct_predictions, accuracy = \ 
      sess.run([correct_predictions_op, accuracy_op], 
        feed_dict={x_ph:self.data.training_features, y_ph:self.data.training_labels_1hot}) 
     print 
     print "Predictions on training data:" 
     print correct_predictions 
     print "Training accuracy = %.3f" % accuracy 

     # Compute accuracy on test set. 
     correct_predictions, accuracy = \ 
      sess.run([correct_predictions_op, accuracy_op], 
        feed_dict={x_ph:self.data.test_features, y_ph:self.data.test_labels_1hot}) 
     print 
     print "Predictions on test data:" 
     print correct_predictions 
     print "Test accuracy = %.3f" % accuracy 

     return model 


def main(): 
    iris_classifier = IrisClassifier() 

    # Load data from CSV files. 
    iris_classifier.loadData() 

    # Train the model. 
    model = iris_classifier.trainModel() 

    # Do something with 'model' if needed. 


if __name__ == "__main__": 
    main() 

スクリプトを実行しているから出力されます：

Epoch  0/ 10000: J= 2.12756 
Epoch 1000/ 10000: J= 0.64714 
Epoch 2000/ 10000: J= 0.57977 
Epoch 3000/ 10000: J= 0.56373 
Epoch 4000/ 10000: J= 0.55583 
Epoch 5000/ 10000: J= 0.54979 
Epoch 6000/ 10000: J= 0.54464 
Epoch 7000/ 10000: J= 0.54016 
Epoch 8000/ 10000: J= 0.53621 
Epoch 9000/ 10000: J= 0.53270 
Training took 5.54 seconds 

Predictions on training data: 
[ True True True True True True True True True True True True 
    True True True True True True True True True True True True 
    True True True True True False True True True True True True 
    True True True True True True True True True True True True 
    True True True True True True True True True True True True 
    True True True True True True True True True True True True 
    True True True True True True True True True True True True 
    True True True True False True True True True True True True 
    True True False True True True True True True True True True 
    True True True True True True True True True True True True] 
Training accuracy = 0.975 

Predictions on test data: 
[ True True True True True True True True True True True True 
    True True True True True True True True True True True False 
    True True True True True True] 
Test accuracy = 0.967