Eu tenho 12 recursos inteiros de entrada. A saída e os rótulos são 1 ou 0. Examino o exemplo MNIST do site tensorflow.
Aqui está o meu código:
import tensorflow as tf
import numpy as np
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def model(X, w):
return tf.matmul(X, w) # notice we use the same model as linear regression, this is because there is a baked in cost function which performs softmax and cross entropy
train_data1 = "./data/xx.csv"
test_data1 = "./data/xxx.csv"
train_label1 = "./data/xxl.csv"
test_label1 = "./data/xx.csv"
train_data = np.genfromtxt(train_data1, delimiter=',')
train_label = np.genfromtxt(train_label1, delimiter=',').astype(int)
test_data = np.genfromtxt(test_data1, delimiter=',')
test_label = np.genfromtxt(test_label1, delimiter=',').astype(int)
# Load datasets.
trX, trY, teX, teY = train_data,train_label, test_data, test_label
X = tf.placeholder("float", [None, 12]) # create symbolic variables
Y = tf.placeholder("float", [None, 2])
w = init_weights([12, 2])
py_x = model(X, w)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) # compute mean cross entropy (softmax is applied internally)
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct optimizer
predict_op = tf.argmax(py_x, 1) # at predict time, evaluate the argmax of the logistic regression
# Launch the graph in a session
with tf.Session() as sess:
# you need to initialize all variables
tf.initialize_all_variables().run()
for i in range(100):
#for (x, y) in zip(train_X, train_Y):
#sess.run(optimizer, feed_dict={X: trX, Y: trY})
for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
print(i, np.mean(np.argmax(teY, axis=1) ==
sess.run(predict_op, feed_dict={X: teX})))
Mas eu executo o código superior, recebo um erro.
Traceback (most recent call last):
File "LRexample.py", line 74, in <module>
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
ValueError: Cannot feed value of shape (128,) for Tensor u'Placeholder_1:0', which has shape '(?, 2)
Como posso lidar com esse erro?
Eu resolvi este problema.
em primeiro lugar, transformei os dados de carga em:
train_data = np.genfromtxt(train_data1, delimiter=',')
train_label = np.transpose(train_label1, delimiter=',')
test_data = np.genfromtxt(test_data1, delimiter=',')
test_label = np.transpose(test_label1, delimiter=',')
Em seguida, transformou os dados trX, trY, teX, teY em:
# convert the data
trX, trY, teX, teY = train_data,train_label, test_data, test_label
temp = trY.shape
trY = trY.reshape(temp[0], 1)
trY = np.concatenate((1-trY, trY), axis=1)
temp = teY.shape
teY = teY.reshape(temp[0], 1)
teY = np.concatenate((1-teY, teY), axis=1)
Por fim, transformei o lançamento do gráfico em uma sessão em:
with tf.Session() as sess:
# you need to initialize all variables
tf.initialize_all_variables().run()
for i in range(100):
sess.run(train_op, feed_dict={X: trX, Y: trY})
print(i, np.mean(np.argmax(teY, axis=1) == sess.run(predict_op, feed_dict={X: teX})))
Isso é tudo.
Comentários muito úteis
Eu resolvi este problema.
em primeiro lugar, transformei os dados de carga em:
Em seguida, transformou os dados trX, trY, teX, teY em:
Por fim, transformei o lançamento do gráfico em uma sessão em:
Isso é tudo.