小编J.D*_*own的帖子

我目前在使用多个输入的网络上使用hyperas优化器时遇到问题.

这就是我实现它的方式:

def data():
    X_train, Y_train = next(train_generator())
    X_test, Y_test = next(test_generator())

    datagen = ImageDataGenerator()
    train_list = []
    for input in X_train:
        train_list.append(datagen.fit(input))

    return datagen, train_list, Y_train, X_test, Y_test

我正在使用data_generator,因为所有数据都不能包含在ram中.根据他们制作的数据示例,我做了这个.

def fws(datagen, X_train, Y_train, X_test, Y_test):
    # Input shape: (batch_size,40,45,3)
    # Output shape: (1,15,50)
    # Number of units in conv_feature_map = splitd
    filter_size = 8
    pooling_size = 28
    stride_step = 2
    pool_splits = ((splits - pooling_size)+1)/2
    temp_list = []
    sun_temp_list = []
    conv_featur_map = []
    pool_feature_map …

当我使用回调函数modelcheckpoint时,我似乎得到了这个错误.

我从github问题中读到了解决方案可以使用的问题model.get_weight,但我隐含地只存储它,因为我只存储具有最佳权重的解决方案.

Keras似乎只使用h5来保存权重,这让我觉得有没有其他方法可以使用eras API来存储它们,如果是这样的话怎么样？如果没有,我该如何存储？

举例来重现这个问题:

#!/usr/bin/python


import glob, os
import sys
from os import listdir
from os.path import isfile, join
import numpy as np
import warnings
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from keras.utils import np_utils
from keras import metrics
import keras
from keras import backend as K
from keras.models import Sequential
from keras.optimizers import SGD, Adam
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten
from keras.layers import Conv1D,Conv2D,MaxPooling2D, MaxPooling1D, Reshape
#from keras.utils.visualize_util import plot
from keras.models …

我有一段时间使用工具keras得到了相当糟糕的结果,并且对这个工具没有那么多的怀疑......但我现在开始有点担心了.

我试图看看它是否可以处理一个简单的XOR问题,并且在30000个时代之后它仍然没有解决它...

码:

from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD
import numpy as np

np.random.seed(100)

model = Sequential()
model.add(Dense(2, input_dim=2))
model.add(Activation('tanh'))
model.add(Dense(1, input_dim=2))
model.add(Activation('sigmoid'))
X = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
y = np.array([[0],[1],[1],[0]], "float32")
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(X, y, nb_epoch=30000, batch_size=1,verbose=1)

print(model.predict_classes(X))

这是我的结果的一部分:

4/4 [==============================] - 0s - loss: 0.3481     
Epoch 29998/30000
4/4 [==============================] - 0s - loss: 0.3481     
Epoch 29999/30000
4/4 [==============================] - 0s - loss: 0.3481     
Epoch 30000/30000
4/4 [==============================] - 0s - loss: 0.3481 …

是否可以使用 Reshape 或任何其他功能删除尺寸。

我有以下网络。

import keras
from keras.layers.merge import Concatenate
from keras.models import Model
from keras.layers import Input, Dense
from keras.layers import Dropout
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten
from keras.layers import Conv2D, MaxPooling2D, Reshape, ZeroPadding2D
import numpy as np


#Number_of_splits = ((input_width-win_dim)+1)/stride_dim
splits = ((40-5)+1)/1
print splits


train_data_1 = np.random.randint(100,size=(100,splits,45,5,3))
test_data_1 = np.random.randint(100,size=(10,splits,45,5,3))
labels_train_data =np.random.randint(145,size=(100,15))
labels_test_data =np.random.randint(145,size=(10,15))


list_of_input = [Input(shape = (45,5,3)) for i in range(splits)]
list_of_conv_output = []
list_of_max_out = []
for i in range(splits):
    list_of_conv_output.append(Conv2D(filters = …

我如何在 columwise 中交织 numpy 矩阵。

给出这个例子：

>>> import numpy as np
>>> a = np.zeros((3,3))
>>> b = np.ones((3,3))
>>> c = b*2

交织输出应该是

[[ a[0,0].  b[0,0].  c[0,0].  a[0,1]  b[0,1]  c[0,1] .. ]
 [ a[1,0].  b[1,0].  c[1,0].  a[1,1]  b[1,1]  c[1,1] .. ]
 [ a[2,0].  b[2,0].  c[2,0].  a[2,1]  b[2,1]  c[2,1] .. ]]

最终形状应为 (3,9)