如何在TensorFlow中連接線性模型

Question

我正在嘗試使用類似f_i（x）= m_ix + b_i的模型在張量流中構建一個模型，以便於：

f(x) = [f_1(x), f_2(x)]^T [x, x] + b

這只是一個練習。 我的困難在於理解如何連接兩個張量：

# Model 1
f1 = tf.add(tf.mul(X, W), b)
# Model 2
f2 = tf.add(tf.mul(X, W2), b2)
# Concatenate 1 & 2
fi = tf.concat(0, [f1, f2])

# Final model
pred = tf.add(tf.mul(fi, W3), b3)

不幸的是，這似乎不起作用。

這是完整的示例：

'''
A linear regression learning algorithm example using TensorFlow library.

Author: Aymeric Damien (original author) # I am altering it though
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

from __future__ import print_function

import tensorflow as tf
import numpy
import matplotlib.pyplot as plt

rng = numpy.random

# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50

# Training Data
train_X = numpy.asarray(
    [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
     7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1])
train_Y = numpy.asarray(
    [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,
     2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3])
n_samples = train_X.shape[0]

# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

W2 = tf.Variable(rng.randn(), name="weight2")
b2 = tf.Variable(rng.randn(), name="bias2")

W3 = tf.Variable([rng.randn(), rng.randn()], name="weight3")
b3 = tf.Variable(rng.randn(), name="bias3")

# Model 1
f1 = tf.add(tf.mul(X, W), b)
# Model 2
f2 = tf.add(tf.mul(X, W2), b2)
# Concatenate 1 & 2
fi = tf.concat(0, [f1, f2])

# Final model
pred = tf.add(tf.mul(fi, W3), b3)

# 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()

# 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)
    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    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()

Answer 1

一種不用tf.concat就可以達到類似結果的tf.concat是

pred = tf.add(tf.add(f1, f2), b3)