我有以下代码错误：
import tensorflow as tf
#from tensorflow.models.rnn import rnn_cell
import numpy as np
import random
class Model():
def __init__(self, args, infer=False):
if infer:
args.batch_size = 1
args.seq_length = 1
self.args = args
if args.model == 'rnn':
cell_fn = tf.nn.rnn_cell.BasicRNNCell
elif args.model == 'gru':
cell_fn = tf.nn.rnn_cell.GRUCell
elif args.model == 'lstm':
cell_fn = tf.nn.rnn_cell.BasicLSTMCell
else:
raise Exception("model type not supported: {}".format(args.model))
cell = cell_fn(args.rnn_size)
#print(args.rnn_size) 256
#print(args.num_layers) 2
#cell = rnn_cell.MultiRNNCell([cell] * args.num_layers)
cell = tf.nn.rnn_cell.MultiRNNCell([cell for _ in range(args.num_layers)])
#print(cell)
if (infer == False and args.keep_prob < 1): # training mode
cell = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob = args.keep_prob)
self.cell = cell
self.input_data = tf.placeholder(dtype=tf.float32, shape=[args.batch_size, args.seq_length, 5])
#print(self.input_data) shape:1 1 5
self.target_data = tf.placeholder(dtype=tf.float32, shape=[args.batch_size, args.seq_length, 5])
#print(self.target_data)
self.initial_state = cell.zero_state(batch_size=args.batch_size, dtype=tf.float32)
self.num_mixture = args.num_mixture
NOUT = 3 + self.num_mixture * 6 # [end_of_stroke + end_of_char, continue_with_stroke] + prob + 2*(mu + sig) + corr
#print(NOUT) 147 num_mixture:24
with tf.variable_scope('rnn_mdn',reuse=tf.AUTO_REUSE):
output_w = tf.get_variable("output_w", [args.rnn_size, NOUT])
output_b = tf.get_variable("output_b", [NOUT])
inputs = tf.split(self.input_data, args.seq_length,1 )
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
#print(inputs) shape:1 5
self.initial_input = np.zeros((args.batch_size, 5), dtype=np.float32)
self.initial_input[:,4] = 1.0 # initially, the pen is down.
self.initial_input = tf.constant(self.initial_input)
#print(self.initial_input) shape:1 5
def tfrepeat(a, repeats):
num_row = a.get_shape()[0].value
num_col = a.get_shape()[1].value
assert(num_col == 1)
result = [a for i in range(repeats)]
result = tf.concat(0, result)
result = tf.reshape(result, [repeats, num_row])
result = tf.transpose(result)
return result
def custom_rnn_autodecoder(decoder_inputs, initial_input, initial_state, cell, scope=None):
# customized rnn_decoder for the task of dealing with end of character
with tf.variable_scope(scope or "rnn_decoder",reuse=tf.AUTO_REUSE):
states = [initial_state]
outputs = []
prev = None
for i in range(len(decoder_inputs)):
inp = decoder_inputs[i]
#print(inp) shape:1 5
if i > 0:
tf.get_variable_scope().reuse_variables()
output, new_state = cell(inp, states[-1])
print(output)
num_batches = self.args.batch_size # new_state.get_shape()[0].value
num_state = new_state.get_shape()[1].value
# if the input has an end-of-character signal, have to zero out the state
#to do: test this code.
eoc_detection = inp[:,3]
eoc_detection = tf.reshape(eoc_detection, [num_batches, 1])
eoc_detection_state = tfrepeat(eoc_detection, num_state)
eoc_detection_state = tf.greater(eoc_detection_state, tf.zeros_like(eoc_detection_state, dtype=tf.float32))
new_state = tf.select(eoc_detection_state, initial_state, new_state)
outputs.append(output)
states.append(new_state)
return outputs, states
outputs, states = custom_rnn_autodecoder(inputs, self.initial_input, self.initial_state, cell, scope='rnn_mdn')
output = tf.reshape(tf.concat(1, outputs), [-1, args.rnn_size])
output = tf.nn.xw_plus_b(output, output_w, output_b)
self.final_state = states[-1]
# reshape target data so that it is compatible with prediction shape
flat_target_data = tf.reshape(self.target_data,[-1, 5])
[x1_data, x2_data, eos_data, eoc_data, cont_data] = tf.split(1, 5, flat_target_data)
pen_data = tf.concat(1, [eos_data, eoc_data, cont_data])
# long method:
#flat_target_data = tf.split(1, args.seq_length, self.target_data)
#flat_target_data = [tf.squeeze(flat_target_data_, [1]) for flat_target_data_ in flat_target_data]
#flat_target_data = tf.reshape(tf.concat(1, flat_target_data), [-1, 3])
def tf_2d_normal(x1, x2, mu1, mu2, s1, s2, rho):
# eq # 24 and 25 of http://arxiv.org/abs/1308.0850
norm1 = tf.sub(x1, mu1)
norm2 = tf.sub(x2, mu2)
s1s2 = tf.mul(s1, s2)
z = tf.square(tf.div(norm1, s1))+tf.square(tf.div(norm2, s2))-2*tf.div(tf.mul(rho, tf.mul(norm1, norm2)), s1s2)
negRho = 1-tf.square(rho)
result = tf.exp(tf.div(-z,2*negRho))
denom = 2*np.pi*tf.mul(s1s2, tf.sqrt(negRho))
result = tf.div(result, denom)
return result
def get_lossfunc(z_pi, z_mu1, z_mu2, z_sigma1, z_sigma2, z_corr, z_pen, x1_data, x2_data, pen_data):
result0 = tf_2d_normal(x1_data, x2_data, z_mu1, z_mu2, z_sigma1, z_sigma2, z_corr)
# implementing eq # 26 of http://arxiv.org/abs/1308.0850
epsilon = 1e-20
result1 = tf.mul(result0, z_pi)
result1 = tf.reduce_sum(result1, 1, keep_dims=True)
result1 = -tf.log(tf.maximum(result1, 1e-20)) # at the beginning, some errors are exactly zero.
result_shape = tf.reduce_mean(result1)
result2 = tf.nn.softmax_cross_entropy_with_logits(z_pen, pen_data)
pen_data_weighting = pen_data[:, 2]+np.sqrt(self.args.stroke_importance_factor)*pen_data[:, 0]+self.args.stroke_importance_factor*pen_data[:, 1]
result2 = tf.mul(result2, pen_data_weighting)
result_pen = tf.reduce_mean(result2)
result = result_shape + result_pen
return result, result_shape, result_pen,
# below is where we need to do MDN splitting of distribution params
def get_mixture_coef(output):
# returns the tf slices containing mdn dist params
# ie, eq 18 -> 23 of http://arxiv.org/abs/1308.0850
z = output
z_pen = z[:, 0:3] # end of stroke, end of character/content, continue w/ stroke
z_pi, z_mu1, z_mu2, z_sigma1, z_sigma2, z_corr = tf.split(1, 6, z[:, 3:])
# process output z's into MDN paramters
# softmax all the pi's:
max_pi = tf.reduce_max(z_pi, 1, keep_dims=True)
z_pi = tf.sub(z_pi, max_pi)
z_pi = tf.exp(z_pi)
normalize_pi = tf.inv(tf.reduce_sum(z_pi, 1, keep_dims=True))
z_pi = tf.mul(normalize_pi, z_pi)
# exponentiate the sigmas and also make corr between -1 and 1.
z_sigma1 = tf.exp(z_sigma1)
z_sigma2 = tf.exp(z_sigma2)
z_corr = tf.tanh(z_corr)
return [z_pi, z_mu1, z_mu2, z_sigma1, z_sigma2, z_corr, z_pen]
[o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_pen] = get_mixture_coef(output)
self.pi = o_pi
self.mu1 = o_mu1
self.mu2 = o_mu2
self.sigma1 = o_sigma1
self.sigma2 = o_sigma2
self.corr = o_corr
self.pen = o_pen # state of the pen
[lossfunc, loss_shape, loss_pen] = get_lossfunc(o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_pen, x1_data, x2_data, pen_data)
self.cost = lossfunc
self.cost_shape = loss_shape
self.cost_pen = loss_pen
self.lr = tf.Variable(0.01, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars), args.grad_clip)
optimizer = tf.train.AdamOptimizer(self.lr, epsilon=0.001)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
def sample(self, sess, num=300, temp_mixture=1.0, temp_pen=1.0, stop_if_eoc = False):
def get_pi_idx(x, pdf):
N = pdf.size
accumulate = 0
for i in range(0, N):
accumulate += pdf[i]
if (accumulate >= x):
return i
print( 'error with sampling ensemble')
return -1
def sample_gaussian_2d(mu1, mu2, s1, s2, rho):
mean = [mu1, mu2]
cov = [[s1*s1, rho*s1*s2], [rho*s1*s2, s2*s2]]
x = np.random.multivariate_normal(mean, cov, 1)
return x[0][0], x[0][1]
prev_x = np.zeros((1, 1, 5), dtype=np.float32)
#prev_x[0, 0, 2] = 1 # initially, we want to see beginning of new stroke
#prev_x[0, 0, 3] = 1 # initially, we want to see beginning of new character/content
prev_state = sess.run(self.cell.zero_state(self.args.batch_size, tf.float32))
strokes = np.zeros((num, 5), dtype=np.float32)
mixture_params = []
for i in range(num):
feed = {self.input_data: prev_x, self.initial_state:prev_state}
[o_pi, o_mu1, o_mu2, o_sigma1, o_sigma2, o_corr, o_pen, next_state] = sess.run([self.pi, self.mu1, self.mu2, self.sigma1, self.sigma2, self.corr, self.pen, self.final_state],feed)
pi_pdf = o_pi[0]
if i > 1:
pi_pdf = np.log(pi_pdf) / temp_mixture
pi_pdf -= pi_pdf.max()
pi_pdf = np.exp(pi_pdf)
pi_pdf /= pi_pdf.sum()
idx = get_pi_idx(random.random(), pi_pdf)
pen_pdf = o_pen[0]
if i > 1:
pi_pdf /= temp_pen # softmax convert to prob
pen_pdf -= pen_pdf.max()
pen_pdf = np.exp(pen_pdf)
pen_pdf /= pen_pdf.sum()
pen_idx = get_pi_idx(random.random(), pen_pdf)
eos = 0
eoc = 0
cont_state = 0
if pen_idx == 0:
eos = 1
elif pen_idx == 1:
eoc = 1
else:
cont_state = 1
next_x1, next_x2 = sample_gaussian_2d(o_mu1[0][idx], o_mu2[0][idx], o_sigma1[0][idx], o_sigma2[0][idx], o_corr[0][idx])
strokes[i,:] = [next_x1, next_x2, eos, eoc, cont_state]
params = [pi_pdf, o_mu1[0], o_mu2[0], o_sigma1[0], o_sigma2[0], o_corr[0], pen_pdf]
mixture_params.append(params)
# early stopping condition
if (stop_if_eoc and eoc == 1):
strokes = strokes[0:i+1, :]
break
prev_x = np.zeros((1, 1, 5), dtype=np.float32)
prev_x[0][0] = np.array([next_x1, next_x2, eos, eoc, cont_state], dtype=np.float32)
prev_state = next_state
strokes[:,0:2] *= self.args.data_scale
return strokes, mixture_params
And the result is as follows:
Traceback (most recent call last):
File "<ipython-input-22-a62f255c6272>", line 1, in <module>
runfile('C:/YMJ/download_pro/magenta-master/magenta/models/sketch_rnn/sketch-rnn-master/sample.py', wdir='C:/YMJ/download_pro/magenta-master/magenta/models/sketch_rnn/sketch-rnn-master')
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile
execfile(filename, namespace)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/YMJ/download_pro/magenta-master/magenta/models/sketch_rnn/sketch-rnn-master/sample.py", line 57, in <module>
model = Model(saved_args, True)
File "C:\YMJ\download_pro\magenta-master\magenta\models\sketch_rnn\sketch-rnn-master\model.py", line 104, in __init__
outputs, states = custom_rnn_autodecoder(inputs, self.initial_input, self.initial_state, cell, scope='rnn_mdn')
File "C:\YMJ\download_pro\magenta-master\magenta\models\sketch_rnn\sketch-rnn-master\model.py", line 81, in custom_rnn_autodecoder
output, new_state = cell(inp, states[-1])
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 183, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\layers\base.py", line 575, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 1066, in call
cur_inp, new_state = cell(cur_inp, cur_state)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 183, in __call__
return super(RNNCell, self).__call__(inputs, state)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\layers\base.py", line 575, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 441, in call
value=self._linear([inputs, h]), num_or_size_splits=4, axis=1)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\rnn_cell_impl.py", line 1189, in __call__
res = math_ops.matmul(array_ops.concat(args, 1), self._weights)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\math_ops.py", line 1891, in matmul
a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 2436, in _mat_mul
name=name)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 2958, in create_op
set_shapes_for_outputs(ret)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 2209, in set_shapes_for_outputs
shapes = shape_func(op)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 2159, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimensions must be equal, but are 512 and 261 for 'rnn_mdn_19/rnn_mdn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/MatMul_1' (op: 'MatMul') with input shapes: [1,512], [261,1024].
我该如何解决？