Tensorflow 1.0.1 Keras 2.0和Python 3.4
我正在使用UFCNN模型进行回归训练，遵循lukovkin / ufcnn-keras＆＃39; ufcnn-keras / notebook / UFCNN.ipynb（＆＃34; https://github.com/lukovkin/ufcnn-keras/tree/master/notebook＆＃34;）和keras功能API教程的模型。 但错误表明＆＃34; ValueError：检查模型输入时出错：预期输入具有形状（无，64,1）但是具有形状（6400,1,1）和＃34;的数组。我希望有人可以帮助我。以下是我的代码：
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import keras
from keras.models import Model
from keras.models import Sequential
from keras.layers import Input, merge
from keras.layers.core import Activation
from keras.layers.convolutional import Conv1D
import matplotlib.pyplot as plt
from keras.preprocessing import sequence
## UFCNN function
def ufcnn_regression_model(sequence_length=5000,
features=1,
nb_filter=150,
filter_length=5,
output_dim=1,
optimizer='adagrad',
loss='mse'):
inputs = Input(shape=(sequence_length, features), name = 'input')
#########################################################
conv_1 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(inputs)
relu_1 = Activation('relu')(conv_1)
#########################################################
conv_2 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(relu_1)
relu_2 = Activation('relu')(conv_2)
#########################################################
conv_3 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(relu_2)
relu_3 = Activation('relu')(conv_3)
#########################################################
conv_4 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(relu_3)
relu_4 = Activation('relu')(conv_4)
#########################################################
merge_1 = keras.layers.add([relu_2, relu_4])
conv_5 =Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(merge_1)
relu_5 = Activation('relu')(conv_5)
#########################################################
merge_2 = keras.layers.add([relu_1, relu_5])
conv_6 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(merge_2)
relu_6 = Activation('relu')(conv_6)
#########################################################
conv_7 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(relu_6)
#########################################################
model = Model(inputs = inputs, outputs = conv_7)
model.compile(optimizer=optimizer, loss=loss)
print(model.summary())
return model
## Input & Output function
def gen_cosine_amp(amp=100, period=25, x0=0, xn=500, step=1, k=0.0001):
cos = np.zeros(((xn - x0) * step, 1, 1))
print("Cos. Shape",cos.shape)
for i in range(len(cos)):
idx = x0 + i * step
cos[i, 0, 0] = amp * np.cos(idx / (2 * np.pi * period))
cos[i, 0, 0] = cos[i, 0, 0] * np.exp(-k * idx)
lahead = 1
expected_output = np.zeros((len(cos), 1))
for i in range(len(cos) - lahead):
expected_output[i, 0] = np.mean(cos[i + 1:i + lahead + 1])
return cos, expected_output
## Parameter
sequence_length = 64
features = 1
nb_filter = 150
filter_length = 5
output_dim = 1
epochs = 5
batch_size = 128
## UFCNN_1 model summary
UFCNN_1 = ufcnn_regression_model(sequence_length=sequence_length)
## Inputs and ouputs to be trained
cos = gen_cosine_amp(xn = sequence_length * 100)[0]
expected_output = gen_cosine_amp(xn = sequence_length * 100)[1]
## Trainning
for i in range(epochs):
print('Epoch', i, '/', epochs)
UFCNN_1.fit(cos, expected_output, verbose=1,epochs=1,shuffle=False, batch_size=batch_size)
print('Predicting')
## Predicting
predicted_output = model.predict(cos, batch_size=batch_size)
我的错误是：
Epoch 0 / 5
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-d49a856b74bd> in <module>()
1 for i in range(epochs):
2 print('Epoch', i, '/', epochs)
----> 3 UFCNN_1.fit(cos, expected_output, verbose=1,epochs=1,shuffle=False, batch_size=batch_size)
4 print('Predicting')
5 predicted_output = model.predict(cos, batch_size=batch_size)
/usr/local/lib/python3.4/dist-packages/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, **kwargs)
1403 class_weight=class_weight,
1404 check_batch_axis=False,
-> 1405 batch_size=batch_size)
1406 # prepare validation data
1407 if validation_data:
/usr/local/lib/python3.4/dist-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1293 self._feed_input_shapes,
1294 check_batch_axis=False,
-> 1295 exception_prefix='model input')
1296 y = _standardize_input_data(y, self._feed_output_names,
1297 output_shapes,
/usr/local/lib/python3.4/dist-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
131 ' to have shape ' + str(shapes[i]) +
132 ' but got array with shape ' +
--> 133 str(array.shape))
134 return arrays
135
ValueError: Error when checking model input: expected input to have shape (None, 64, 1) but got array with shape (6400, 1, 1)
感谢您的帮助，让它发挥作用!!!!!
BTW：这个模型与lukovkin / ufcnn-keras几乎相同。 model，仅用于更新代码以适应较新版本的keras和tensorflow。答案 0 :(得分：2)
Theano 1.0.0，Keras 2.1.3，Python 2.7.10
输入和输出的形状：对于我对keras和CNN的理解（但是很窄），您需要在形状(n_samples, sequence_length, features)的样本上训练网络。这意味着将您的时间序列扩展为rolling window view，将形状(6400, 1, 1)广播到(6400, 64, 1)。您可以通过as_strided执行此操作：
## reshape data
cos_view = np.lib.stride_tricks.as_strided(
cos,
shape=[cos.shape[0] - sequence_length, sequence_length, 1],
strides=cos.strides
)
expected_output_view = np.lib.stride_tricks.as_strided(
expected_output,
shape=[expected_output.shape[0] - sequence_length, sequence_length, 1],
strides=expected_output.strides
)
但是expected_output也必须具有更高的维度。在gen_cosine_amp：
def gen_cosine_amp(amp=100, period=25, x0=0, xn=500, step=1, k=0.0001):
...
expected_output = np.zeros((len(cos), 1, 1))
for i in range(len(cos) - lahead):
expected_output[i, 0, 0] = np.mean(cos[i + 1:i + lahead + 1])
现在可以在这些广播视图上训练你的模型：
UFCNN_1.fit(cos_view, expected_output_view, verbose=1,epochs=1,shuffle=False, batch_size=batch_size)
修复模型错误但请注意，您的代码中还有其他错误。图层conv_7是您的输出图层。其输出/过滤器维度不应为nb_filter，而应为output_dim
conv_7 = Conv1D(filters=output_dim, kernel_size=filter_length, padding='same')(relu_6)
关注点尽管通过这些修改对数据进行了很好的训练，但我认为这个模型将会延伸到时间序列的未来（假设你的信号是时间序列，就像在lukovkin＆ ＃39; s ufcnn-keras）。也许Conv1D - padding="causal"和dilation_rate=x（x> 1）的图层会更好。 （我还在尝试时间序列预测）
此外，请注意，模型通过add合并图层，而不是concatenate，这类似于Roni Mittelman文件中描述的UFCNN模型。 以下代码并未反映这些问题。
全部放在一起：
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import keras
from keras.models import Model
from keras.models import Sequential
from keras.layers import Input, merge
from keras.layers.core import Activation
from keras.layers.convolutional import Conv1D
import matplotlib.pyplot as plt
from keras.preprocessing import sequence
def ufcnn_regression_model(sequence_length=5000,
features=1,
nb_filter=150,
filter_length=5,
output_dim=1,
optimizer='adagrad',
loss='mse'):
inputs = Input(shape=(sequence_length, features), name = 'input')
#########################################################
conv_1 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(inputs)
relu_1 = Activation('relu')(conv_1)
#########################################################
conv_2 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(relu_1)
relu_2 = Activation('relu')(conv_2)
#########################################################
conv_3 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(relu_2)
relu_3 = Activation('relu')(conv_3)
#########################################################
conv_4 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(relu_3)
relu_4 = Activation('relu')(conv_4)
#########################################################
merge_1 = keras.layers.add([relu_2, relu_4])
conv_5 =Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(merge_1)
relu_5 = Activation('relu')(conv_5)
#########################################################
merge_2 = keras.layers.add([relu_1, relu_5])
conv_6 = Conv1D(filters=nb_filter, kernel_size=filter_length, padding='same')(merge_2)
relu_6 = Activation('relu')(conv_6)
#########################################################
conv_7 = Conv1D(filters=output_dim, kernel_size=filter_length, padding='same')(relu_6)
#########################################################
model = Model(inputs = inputs, outputs = conv_7)
model.compile(optimizer=optimizer, loss=loss)
print(model.summary())
return model
## Input & Output function
def gen_cosine_amp(amp=100, period=25, x0=0, xn=500, step=1, k=0.0001):
cos = np.zeros(((xn - x0) * step, 1, 1))
print("Cos. Shape",cos.shape)
for i in range(len(cos)):
idx = x0 + i * step
cos[i, 0, 0] = amp * np.cos(idx / (2 * np.pi * period))
cos[i, 0, 0] = cos[i, 0, 0] * np.exp(-k * idx)
lahead = 1
expected_output = np.zeros((len(cos), 1, 1))
for i in range(len(cos) - lahead):
expected_output[i, 0, 0] = np.mean(cos[i + 1:i + lahead + 1])
return cos, expected_output
## Parameter
sequence_length = 64
features = 1
nb_filter = 150
filter_length = 5
output_dim = 1
epochs = 5
batch_size = 128
## UFCNN_1 model summary
UFCNN_1 = ufcnn_regression_model(sequence_length=sequence_length, nb_filter=nb_filter)
## Inputs and ouputs to be trained
cos, expected_output = gen_cosine_amp(xn = sequence_length * 100)
## reshape data
cos_view = np.lib.stride_tricks.as_strided(
cos,
shape=[cos.shape[0] - sequence_length, sequence_length, 1],
strides=cos.strides
)
expected_output_view = np.lib.stride_tricks.as_strided(
expected_output,
shape=[expected_output.shape[0] - sequence_length, sequence_length, 1],
strides=expected_output.strides
)
print("Cos. Shape Input ",cos_view.shape)
## Trainning
for i in range(epochs):
print('Epoch', i, '/', epochs)
UFCNN_1.fit(cos_view, expected_output_view, verbose=1,epochs=1,shuffle=False, batch_size=batch_size)
print('Predicting')
predicted_output = UFCNN_1.predict(cos_view, batch_size=batch_size)
rmse = np.sqrt(((predicted_output - cos_view) ** 2).mean(axis=None))
print ("RMSE ", rmse)