我想构建一个神经网络，当给定一组更大的点（作为控制曲线的一部分）时，该神经网络将生成贝塞尔曲线的参数（其控制点）。
换句话说，穿过网格的贝塞尔曲线是什么。我正在用它自学TensorFlow。到目前为止，使用我已构建的代码，我得到了“使用输入dim 3索引超出范围；输入只有3个dims [Op：StridedSlice]名称：strided_slice”，但是欢迎对该方法进行总体建议！
geomdl是一个从控制点生成网格的库，我正在使用它来生成训练。
# this program has the following structure:
# input = 100 pairs of x-y points
# output = 8 pairs of x-y that are the control points of a bezier curve
from geomdl import BSpline
from geomdl import utilities
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import random
import numpy as np
tf.enable_eager_execution()
我从8个控制点生成100个点的网格
# those are utilities to generate the training data
n_evalpts = 100
def evalpts(ctrlpts):
c = BSpline.Curve()
c.degree = 2
c.ctrlpts = ctrlpts
c.delta = 1/(n_evalpts-0.5)
c.knotvector = utilities.generate_knot_vector(c.degree, len(c.ctrlpts))
c.render
return c.evalpts
def mevalpts(ctrlptslist):
evalptslist=[]
for i in range(len(ctrlptslist)):
evalptslist.append(evalpts(ctrlptslist[i]))
return evalptslist
#curve = evalpts([[5.0, 10.0], [15.0, 25.0], [30.0, 30.0], [45.0, 5.0], [55.0, 5.0],
# [70.0, 40.0], [60.0, 60.0], [35.0, 60.0]])
现在生成训练数据
#generate X samples of inputs (100 pairs of points) and outputs (8 pairs)
NUM_SAMPLES = 25
outputs = tf.random_uniform(shape=[NUM_SAMPLES, 8,2], maxval=5)
inputs = np.array(mevalpts(outputs.numpy().tolist())) #shape [n_evalpts,2]
#inputs = tf.Variable(mevalpts(outputs.numpy().tolist()))
print(inputs.shape)
print(outputs.shape)
定义模型（尝试具有自定义损失函数，但已放弃）
def model():
model = keras.Sequential([
layers.Flatten(input_shape=(n_evalpts,2)),
layers.Dense(64, activation=tf.nn.relu),
layers.Dense(64, activation=tf.nn.relu),
layers.Dense(32, activation=tf.nn.relu),
layers.Dense(16)
])
optimizer = tf.keras.optimizers.RMSprop(0.001)
model.compile(loss=loss, #'mean_squared_error',
optimizer=optimizer,
metrics=['mean_absolute_error', 'mean_squared_error'])
return model
model = model()
拟合数据，但这不起作用
EPOCHS = 100
history = model.fit(
inputs,
outputs,
epochs=EPOCHS,
#validation_split = 0.2,
#verbose=0
)
更新：
我在最后一个单元格上得到的确切错误如下：
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-9-9994db909e15> in <module>
4 inputs,
5 outputs,
----> 6 epochs=EPOCHS,
7 #validation_split = 0.2,
8 #verbose=0
/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)
878 initial_epoch=initial_epoch,
879 steps_per_epoch=steps_per_epoch,
--> 880 validation_steps=validation_steps)
881
882 def evaluate(self,
/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, mode, validation_in_fit, **kwargs)
308 if ins and isinstance(ins[-1], int):
309 # Do not slice the training phase flag.
--> 310 ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
311 else:
312 ins_batch = slice_arrays(ins, batch_ids)
/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py in slice_arrays(arrays, start, stop)
524 if hasattr(start, 'shape'):
525 start = start.tolist()
--> 526 return [None if x is None else x[start] for x in arrays]
527 else:
528 return [None if x is None else x[start:stop] for x in arrays]
/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py in <listcomp>(.0)
524 if hasattr(start, 'shape'):
525 start = start.tolist()
--> 526 return [None if x is None else x[start] for x in arrays]
527 else:
528 return [None if x is None else x[start:stop] for x in arrays]
/usr/local/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py in _slice_helper(tensor, slice_spec, var)
652 ellipsis_mask=ellipsis_mask,
653 var=var,
--> 654 name=name)
655
656
/usr/local/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py in strided_slice(input_, begin, end, strides, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask, var, name)
818 ellipsis_mask=ellipsis_mask,
819 new_axis_mask=new_axis_mask,
--> 820 shrink_axis_mask=shrink_axis_mask)
821
822 parent_name = name
/usr/local/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py in strided_slice(input, begin, end, strides, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask, name)
9332 else:
9333 message = e.message
-> 9334 _six.raise_from(_core._status_to_exception(e.code, message), None)
9335 # Add nodes to the TensorFlow graph.
9336 if begin_mask is None:
/usr/local/lib/python3.7/site-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: Index out of range using input dim 3; input has only 3 dims [Op:StridedSlice] name: strided_slice/
答案 0 :(得分：0)
我能够在这里建立我的网络。一个基本的错误是我的模型生成的是平面（16,1）数组，但是我输入的是（8,2）真数组。
我将最后一个单元格更改为：
EPOCHS = 1000
history = model.fit(
inputs,
np.reshape(outputs,(NUM_SAMPLES,16)), #previously this was outputs
epochs=EPOCHS,
validation_split = 0.2,
#verbose=0
)