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我想在张量流中实现多元回归,其中我有192个具有6个特征和一个输出变量的示例。从我的模型中,我得到一个矩阵(192,6),而它应该是(192,1)。有人知道我的代码有什么问题吗?我在下面提供了我的代码。使用TensorFlow计算多元回归
# Parameters
learning_rate = 0.0001
training_epochs = 50
display_step = 5
train_X = Data_ABX3[0:192, 0:6]
train_Y = Data_ABX3[0:192, [24]]
# placeholders for a tensor that will be always fed.
X = tf.placeholder('float', shape = [None, 6])
Y = tf.placeholder('float', shape = [None, 1])
# Training Data
n_samples = train_Y.shape[0]
# Set model weights
W = tf.cast(tf.Variable(rng.randn(1, 6), name="weight"), tf.float32)
b = tf.Variable(rng.randn(), name="bias")
# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)
# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
# Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Accuracy
# #accuracy = tf.contrib.metrics.streaming_accuracy(Y, pred)
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Start training
with tf.Session() as sess:
# Run the initializer
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: train_X, Y: train_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 = 0
#for (x, y) in zip(train_X, train_Y):
# tr_cost = sess.run(cost, feed_dict={X: x, Y: y})
# training_cost += tr_cost
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_Y, train_X * sess.run(W) + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
谢谢Rachit。我使用它并且没有工作,我得到这个消息:ValueError:尺寸必须相等,但对于'MatMul'(op:'MatMul'),其输入形状为[?,6],[1, 6]。 – Hamid
@Hamid不知道你的输入数据,但我用你的代码随机数据。 –
非常感谢您的有用评论。我更改了代码的这些部分:“Data_ABX3 = numpy.loadtxt(file,dtype ='float32',...”以及“W = tf.cast(tf.Variable(tf.zeros([6,1])) ,...“。现在它正在工作,但我得到越来越高的成本(培训成本= 4.81842e + 28),同时我增加了training_epochs。 – Hamid