Question

我正在使用TensorFlow实现网络。网络将二进制特征向量作为输入，它应该将浮点值预测为输出。我期待一个（1,1）张量对象作为我的函数multilayer_perceptron()的输出，相反，当运行pred时，它返回一个与我的输入数据（X，1）长度相同的向量。

由于我是这个框架的新手，我希望错误非常简单。我做错了什么？

import tensorflow as tf

print "**** Defining parameters..."
# Parameters
learning_rate = 0.001 
training_epochs = 15
batch_size = 1
display_step = 1

print "**** Defining Network..."
# Network Parameters
n_hidden_1 = 10 # 1st layer num features
n_hidden_2 = 10 # 2nd layer num features
n_input = Xa.shape[1] # data input(feature vector length) 
n_classes = 1 # total classes (IC50 value)

# tf Graph input
x = tf.placeholder("int32", [batch_size, None])
y = tf.placeholder("float", [None, n_classes])

# Create model
def multilayer_perceptron(_X, _weights, _biases):
  lookup_h1 = tf.nn.embedding_lookup(_weights['h1'], _X)
  layer_1 = tf.nn.relu(tf.add(tf.reduce_sum(lookup_h1, 0), _biases['b1'])) #Hidden layer with RELU activation
  layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) #Hidden layer with RELU activation
  pred = tf.matmul(layer_2, _weights['out']) + _biases['out']

  return pred

# Store layers weight & bias
weights = {
          'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
          'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
          'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
          }
biases = {
      'b1': tf.Variable(tf.random_normal([n_hidden_1])),
      'b2': tf.Variable(tf.random_normal([n_hidden_2])),
      'out': tf.Variable(tf.random_normal([n_classes]))
}

# Construct model
pred = multilayer_perceptron(x, weights, biases)

# Define loss and optimizer
cost = tf.reduce_mean(tf.square(tf.sub(pred, y))) # MSE
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent

# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.initialize_all_variables()

print "**** Launching the graph..."
# Launch the graph
with tf.Session() as sess:
  sess.run(init)

  print "**** Training..."
  # Training cycle
  for epoch in range(training_epochs):
    avg_cost = 0.
    total_batch = int(Xa.tocsc().shape[0]/batch_size)
    # Loop over all batches
    for i in range(total_batch):
      # Extract sample
      batch_xs = Xa.tocsc()[i,:].tocoo()
      batch_ys = np.reshape(Ya.tocsc()[i,0], (batch_size,1))
      #****************************************************************************
      # Extract sparse indeces from input matrix (They will be used as actual input)
      ids = batch_xs.nonzero()[1]
      # Fit training using batch data
      sess.run(optimizer, feed_dict={x: ids, y: batch_ys})
      # Compute average loss
      avg_cost += sess.run(cost, feed_dict={x: ids, y: batch_ys})/total_batch
      # Display logs per epoch step
    if epoch % display_step == 0:
      print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
  print "Optimization Finished!"

Answer 1

您的pred应该是[n_input, n_class]的形状，因为您以这种方式定义weights['out']和biases['out']。从[{1}}获得（1,1）张量的唯一方法是pred ...

TensorFlow：网络输出没有预期的形状

1 个答案: