我希望使用Tensorflow训练神经网络模型进行分类，我想从CSV文件中读取数据，例如Iris数据集。
Tensorflow documentation显示了加载Iris数据和构建预测模型的示例，但该示例使用了高级tf.contrib.learn API。我想使用低级Tensorflow API并自己运行渐变下降。我该怎么做？
答案 0 :(得分：3)
下面是一个完整的脚本，用于从CSV文件加载Iris数据并训练2层神经网络。您可以从Tensorflow example page下载CSV文件（培训和测试集），但您需要先从每个文件中删除标题行。
#!/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