Как Concat линейных моделей в TensorFlow

Question

Я пытаюсь построить модель в тензорном потоке с моделями, как F_i (х) = m_ix + b_i, что:

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

это просто упражнение. Моя трудность заключается в понимании того, как конкатенировать два тензора:

# 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) 

К сожалению, это не работает.

Вот полный пример:

''' 
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

Одним из способов достижения аналогичного результата без головной болью tf.concat является

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