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我在TensorFlow中编写了一个用于线性分类的代码。我生成假数据的基础是“如果差值大于x（某些常数），输出应该是[1,0]，否则输出应该是[0,1]。这是我的代码TensorFlow分类与实际输出不匹配
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def main(_):
# Import data
# Create the model
x = tf.placeholder(tf.float32, [None, 2])
W1 = weight_variable([2, 2])
b1 = bias_variable([2])
y2 = tf.nn.softmax(tf.matmul(x, W1) + b1)
# Define loss and optimizer'''
y_ = tf.placeholder(tf.float32, [None, 2])
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y2))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
for _ in range(10000):
batch_xs, batch_ys = data_supplier.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
test_batch_x, test_batch_y = data_supplier.test_data()
correct_prediction = tf.equal(tf.argmax(y2, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: test_batch_x,
y_: test_batch_y}))
print(x.eval(feed_dict={x: test_batch_x, y_: test_batch_y}))
print(y2.eval(feed_dict={x: test_batch_x, y_: test_batch_y}))
print(y_.eval(feed_dict={x: test_batch_x, y_: test_batch_y}))
print(W1.eval())
print(b1.eval())
print(cross_entropy.eval(feed_dict={x: test_batch_x, y_: test_batch_y}))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
而这里的data_supplier代码：
TOTAL_DATA_SIZE = 50000
TRAIN_DATA_SIZE = 40000
VALIDATION_DATA_SIZE = 5000
TEST_DATA_SIZE = 5000
COLUMNS = ["a", "b","output", "outputbar"]
FEATURES = ["a", "b"]
LABELS = ["output", "outputbar"]
training_set = pd.read_csv("train.csv", skipinitialspace=True, skiprows=1, names=COLUMNS)
training_set_features = training_set.as_matrix(columns=FEATURES)
training_set_labels = training_set.as_matrix(columns=LABELS)
test_set = pd.read_csv("test.csv", skipinitialspace=True, skiprows=1, names=COLUMNS)
test_set_features = test_set.as_matrix(columns=FEATURES)
test_set_labels = test_set.as_matrix(columns=LABELS)
def next_batch(BATCH_SIZE):
k = np.random.randint(0,TRAIN_DATA_SIZE-BATCH_SIZE)
return training_set_features[k:k+BATCH_SIZE], training_set_labels[k:k+BATCH_SIZE]
def test_data():
return test_set_features, test_set_labels
而这里的输出：
accuracy: 0.6852
Input: [[ 0.51166666 0.79333335]
[ 0.85833335 0.21833333]
[ 0.80333334 0.48333332]
...,
[ 0.28333333 0.96499997]
[ 0.97666669 0.84833336]
[ 0.57666665 0.21333334]]
Predictions: [[ 0.80804855 0.19195142]
[ 0.78380686 0.21619321]
[ 0.80210352 0.19789645]
...,
[ 0.80708122 0.19291875]
[ 0.83949649 0.16050354]
[ 0.76328743 0.23671262]]
Actual output: [[ 0. 1.]
[ 1. 0.]
[ 1. 0.]
...,
[ 1. 0.]
[ 1. 0.]
[ 1. 0.]]
Weights: [[ 0.3034386 -0.10369452]
[ 0.29422989 -0.21103808]]
Bias: [ 0.5141086 -0.3141087]
Cross Entropy: 0.624272
，因为每个预测分为精度是目前意义[1,0]什么是错误的我在做？