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我正在使用Tensorflow的RBF網絡上工作，但是有這樣的錯誤出現在第112行，它表示：ValueError：無法爲Tensor'佔位符：0'提供形狀值（40,13），它具有形狀'（？，12）'Tensorflow中我的RBF網絡問題？
下面是我的代碼如下。我通過遵循this tutorial爲我的RBF網絡創建了自己的激活功能。另外，如果您還有其他問題需要解決，請向我指出，因爲我對Tensorflow非常陌生，因此獲得我可以獲得的任何反饋信息都會有幫助。
import tensorflow as tf
import numpy as np
import math
from sklearn import datasets
from sklearn.model_selection import train_test_split
from tensorflow.python.framework import ops
ops.reset_default_graph()
RANDOM_SEED = 42
tf.set_random_seed(RANDOM_SEED)
boston = datasets.load_boston()
data = boston["data"]
target = boston["target"]
N_INSTANCES = data.shape[0]
N_INPUT = data.shape[1] - 1
N_CLASSES = 3
TEST_SIZE = 0.1
TRAIN_SIZE = int(N_INSTANCES * (1 - TEST_SIZE))
batch_size = 40
training_epochs = 400
learning_rate = 0.001
display_step = 20
hidden_size = 200
target_ = np.zeros((N_INSTANCES, N_CLASSES))
data_train, data_test, target_train, target_test = train_test_split(data, target_, test_size=0.1, random_state=100)
x_data = tf.placeholder(shape=[None, N_INPUT], dtype=tf.float32)
y_target = tf.placeholder(shape=[None, N_CLASSES], dtype=tf.float32)
# creates activation function
def gaussian_function(input_layer):
initial = math.exp(-2*math.pow(input_layer, 2))
return initial
np_gaussian_function = np.vectorize(gaussian_function)
def d_gaussian_function(input_layer):
initial = -4 * input_layer * math.exp(-2*math.pow(input_layer, 2))
return initial
np_d_gaussian_function = np.vectorize(d_gaussian_function)
np_d_gaussian_function_32 = lambda input_layer: np_d_gaussian_function(input_layer).astype(np.float32)
def tf_d_gaussian_function(input_layer, name=None):
with ops.name_scope(name, "d_gaussian_function", [input_layer]) as name:
y = tf.py_func(np_d_gaussian_function_32, [input_layer],[tf.float32], name=name, stateful=False)
return y[0]
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
rnd_name = 'PyFunGrad' + str(np.random.randint(0, 1E+8))
tf.RegisterGradient(rnd_name)(grad)
g = tf.get_default_graph()
with g.gradient_override_map({"PyFunc": rnd_name}):
return tf.py_func(func, inp, Tout, stateful=stateful, name=name)
def gaussian_function_grad(op, grad):
input_variable = op.inputs[0]
n_gr = tf_d_gaussian_function(input_variable)
return grad * n_gr
np_gaussian_function_32 = lambda input_layer: np_gaussian_function(input_layer).astype(np.float32)
def tf_gaussian_function(input_layer, name=None):
with ops.name_scope(name, "gaussian_function", [input_layer]) as name:
y = py_func(np_gaussian_function_32, [input_layer], [tf.float32], name=name, grad=gaussian_function_grad)
return y[0]
# end of defining activation function
def rbf_network(input_layer, weights):
layer1 = tf.matmul(tf_gaussian_function(input_layer), weights['h1'])
layer2 = tf.matmul(tf_gaussian_function(layer1), weights['h2'])
output = tf.matmul(tf_gaussian_function(layer2), weights['output'])
return output
weights = {
'h1': tf.Variable(tf.random_normal([N_INPUT, hidden_size], stddev=0.1)),
'h2': tf.Variable(tf.random_normal([hidden_size, hidden_size], stddev=0.1)),
'output': tf.Variable(tf.random_normal([hidden_size, N_CLASSES], stddev=0.1))
}
pred = rbf_network(x_data, weights)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y_target))
my_opt = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y_target, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
sess = tf.InteractiveSession()
sess.run(init)
# Training loop
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(data_train.shape[0]/batch_size)
for i in range(total_batch):
randidx = np.random.randint(int(TRAIN_SIZE), size=batch_size)
batch_xs = data_train[randidx, :]
batch_ys = target_train[randidx, :]
sess.run(my_opt, feed_dict={x_data: batch_xs, y_target: batch_ys})
avg_cost += sess.run(cost, feed_dict={x_data: batch_xs, y_target: batch_ys})/total_batch
if epoch % display_step == 0:
print("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
train_accuracy = sess.run(accuracy, feed_dict={x_data: batch_xs, y_target: batch_ys})
print("Training accuracy: %.3f" % train_accuracy)
test_acc = sess.run(accuracy, feed_dict={x_data: data_test, y_target: target_test})
print("Test accuracy: %.3f" % (test_acc))
sess.close()
