Python 使用完整的Tensorflow从Iris CSV数据进行培训

我想用Tensorflow来训练神经网络模型进行分类，我想从CSV文件中读取数据，比如虹膜数据集

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显示了加载Iris数据和构建预测模型的示例，但该示例使用高级

tf.contrib.learn

API。我想使用低级的Tensorflow API，自己运行梯度下降。我该怎么做呢？

下面是一个完整的脚本，用于从CSV文件加载虹膜数据和训练两层神经网络。您可以从下载CSV文件（培训和测试集），但需要先删除每个文件的标题行

#!/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()

以下是运行脚本的输出：

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