这里train_X（shape = m，64）和train_Y（m，1）是numpy数组，其中m是我的训练样本数。
print('train and test array computed')
print(train_X[:2])
print(train_Y[:2])
这些行的输出是
train and test array computed
[[ 8.10590000e+01 8.91460000e+01 1.00000000e+02 1.00000000e+02
0.00000000e+00 5.26000000e-01 5.80000000e-01 5.80000000e-01
5.80000000e-01 5.80000000e-01 8.00000000e-01 6.66670000e+01
6.36160000e+01 8.36000000e-01 1.17300000e+00 5.80000000e-01
6.48860000e+01 5.80000000e-01 1.73640000e+01 -2.07250000e+01
5.81000000e-01 0.00000000e+00 5.80000000e-01 5.80000000e-01
7.00000000e-03 5.80000000e-01 -5.44000000e-01 -2.36000000e-01
5.80000000e-01 5.81000000e-01 5.81000000e-01 5.80000000e-01
5.80000000e-01 5.80000000e-01 5.80000000e-01 5.80000000e-01
0.00000000e+00 4.00000000e-01 2.10000000e-02 1.00000000e+00
1.00021000e+02 8.18080000e+01 5.71000000e-01 5.48000000e-01
6.14000000e-01 5.80000000e-01 7.62000000e-01 0.00000000e+00
1.00000000e+02 1.00000000e+02 0.00000000e+00 1.00000000e+02
1.00000000e+02 1.16100000e+00 5.80000000e-01 6.56000000e-01
5.80000000e-01 5.80000000e-01 5.81000000e-01 5.80000000e-01
1.00000000e+02 5.80000000e-01 0.00000000e+00 5.80000000e-01]
[ 5.12680000e+01 4.87480000e+01 1.00000000e+02 1.00000000e+02
0.00000000e+00 4.18000000e-01 4.44000000e-01 4.44000000e-01
4.44000000e-01 4.44000000e-01 5.00000000e-01 6.66670000e+01
3.83570000e+01 9.03000000e-01 1.10000000e+00 4.44000000e-01
5.63070000e+01 4.44000000e-01 1.85220000e+01 1.94233000e+02
4.44000000e-01 0.00000000e+00 4.44000000e-01 4.44000000e-01
1.00000000e-03 4.44000000e-01 -8.12000000e-01 -3.53000000e-01
4.44000000e-01 4.44000000e-01 4.44000000e-01 4.44000000e-01
4.44000000e-01 4.44000000e-01 4.44000000e-01 4.44000000e-01
0.00000000e+00 5.00000000e-01 2.00000000e-03 1.00000000e+00
1.00002000e+02 6.91780000e+01 4.42000000e-01 4.29000000e-01
4.59000000e-01 4.44000000e-01 6.66000000e-01 0.00000000e+00
5.00000000e+01 5.00000000e+01 0.00000000e+00 5.00000000e+01
5.00000000e+01 8.88000000e-01 4.44000000e-01 4.75000000e-01
4.44000000e-01 4.44000000e-01 4.44000000e-01 4.44000000e-01
5.00000000e+01 4.44000000e-01 -5.00000000e+01 4.44000000e-01]]
[ 0.44378 0.6821 ]
以下是我的计划的相关部分。
rng = np.random
# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50
# Training Data
n_samples = train_X.shape[0]
n_features = train_X.shape[1]
# tf Graph Input
X = tf.placeholder(tf.float32,[None,n_features])
Y = tf.placeholder(tf.float32,[None,1])
# Set model weights
W = tf.Variable(tf.random_normal([n_features,1],mean=0,stddev=(np.sqrt(6/n_features+
1+1))), name="weight")
b = tf.Variable(tf.random_normal([1,1],
mean=0,
stddev=(np.sqrt(6/n_features+1+1)),
name="bias"))
# Construct a linear model
pred = tf.add(tf.mul(X, W), b)
# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initializing the variables
init = tf.initialize_all_variables()
print('starting the session')
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
# Display logs per epoch step
if (epoch+1) % display_step == 0:
c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b))
print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
# Graphic display
# plt.plot(train_X, train_Y, 'ro', label='Original data')
# plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
# plt.legend()
# plt.show()
# Testing example, as requested (Issue #2)
print("Testing... (Mean square loss Comparison)")
testing_cost = sess.run(
tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
feed_dict={X: test_X, Y: test_Y}) # same function as cost above
print("Testing cost=", testing_cost)
print("Absolute mean square loss difference:", abs(
training_cost - testing_cost))
# plt.plot(test_X, test_Y, 'bo', label='Testing data')
# plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
# plt.legend()
# plt.show()
我很难让它长时间工作，但它从未奏效。我给了我错误。
Traceback (most recent call last):
File "q.py", line 158, in <module>
sess.run(optimizer, feed_dict={X: x, Y: y})
File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 382, in run
run_metadata_ptr)
File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 640, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (64,) for Tensor 'Placeholder:0', which has shape '(?, 64)'
问题在于 sess.run（optimizer，feed_dict = {X：x，Y：y}）
答案 0 :(得分：1)
我认为错误是你在zip（train_X，train_Y）中拉链（x，y）：所以这将给出一个x和一个y的例子。
你反而希望直接在trainX和trainY中输入：
feed_dict={X: train_X, Y:train_Y}
您可以通过运行
来检查是否属于这种情况for (x, y) in zip(train_X, train_Y):
print(x.shape,y.shape) # I guess this will be 64 and 1 for x and y resp.
sess.run(optimizer, feed_dict={X: x, Y: y})
你希望它有X =（＃，64）
的形状