我目前正在尝试为我想要解决的回归任务设置张量流。
我不确定如何输入我的输入数据，因为它以某种方式存储。
我的输入是一个范围为0.5秒的样本音频文件。每个音频文件都附加到列表中，每个帧都存储为numpy.array。
这样的事情
#One audio set
[array([[frame],[frame],...,[frame]],dtype=float32)]
输出以这种方式存储
[array([[ 4.53255900e+01, -9.82247700e+00, -1.22920200e+00,
-6.81728800e+00, -8.23808400e+00, -1.42367900e+01,
-4.64904100e+00, -1.49673100e+01, -4.75058700e+00,
-7.54071600e+00, 5.11365500e+00, 9.80618400e+00,
-6.03554700e-01],....,[...]])]
我试图实现一些我认为可以工作的东西..但是得到这个错误信息： 代码：
os.chdir(dnn_train)
train_name,train_data = generate_list_of_names_data(files_train_path)
train_data, train_output_data = load_sound_files(files_train_path,train_data)
number_of_examples = len(train_data)/2
train_set_data = train_data[:number_of_examples]
train_set_output = train_output_data[:number_of_examples]
test_set_data = train_data[number_of_examples:]
test_set_output = train_output_data[number_of_examples:]
############################# Training setup ##################################
# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50
# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")
n_samples = train_set_data[0].shape[0]
# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
# Construct a linear model
pred = tf.add(tf.mul(X, W), b)
# 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_set_data, train_set_output):
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_set_data, Y:train_set_output})
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_set_data, Y: train_set_output})
print "Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n'
#Graphic display
plt.plot(train_set_data, train_set_output, 'ro', label='Original data')
plt.plot(train_set_data, sess.run(W) * train_set_data + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
错误：
File "tensorflow_datapreprocess_mfcc_extraction_rnn.py", line 191, in <module>
sess.run(optimizer, feed_dict={X: x, Y: y})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 340, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 564, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 637, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 659, in _do_call
e.code)
tensorflow.python.framework.errors.InvalidArgumentError: Incompatible shapes: [2048,121] vs. [288,13]
[[Node: gradients/sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[_device="/job:localhost/replica:0/task:0/cpu:0"](gradients/sub_grad/Shape, gradients/sub_grad/Shape_1)]]
Caused by op u'gradients/sub_grad/BroadcastGradientArgs', defined at:
File "tensorflow_datapreprocess_mfcc_extraction_rnn.py", line 178, in <module>
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 190, in minimize
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 241, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients.py", line 481, in gradients
in_grads = _AsList(grad_fn(op, *out_grads))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py", line 386, in _SubGrad
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 140, in _broadcast_gradient_args
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2154, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1154, in __init__
self._traceback = _extract_stack()
...which was originally created as op u'sub', defined at:
File "tensorflow_datapreprocess_mfcc_extraction_rnn.py", line 176, in <module>
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 518, in binary_op_wrapper
return func(x, y, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1683, in sub
return _op_def_lib.apply_op("Sub", x=x, y=y, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2154, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1154, in __init__
self._traceback = _extract_stack()
如何将这些信息提供给张量流？
答案 0 :(得分：0)
tf.mul（X，W）只是标量乘法（W是代码中的标量），因此得到的形状与X的形状相同，看起来就像错误的来源（损失函数中的减法可以起作用的唯一方法是X和Y具有相同的形状）。
X和Y在示例中是否具有一致的形状？如果是这样，您可能希望将两者展平为具有tf.reshape（X，[1，-1]）和tf.reshape（Y，[1，-1]）的行向量。我将调用这些向量X_LENGTH和Y_LENGTH的长度。然后我会用一个tf.get_variable（name =＆＃34; W＆＃34;，shape = [X_LENGTH，Y_LENGTH]）使W成为矩阵，然后用tf.matmul（X，W）代替tf.mul。 matmul（）的输出将具有形状[1，Y_LENGTH]，这将解决您在损失函数中遇到的形状问题。
当你在它的时候，你可能想要制作一个长度为Y_LENGTH的向量来获得每个输出的偏差。