我有MNIST数据的CNN代码，它将数据集划分为训练集和测试集，仅用于2和7。在运行它时，代码在测试集上提供大约98％的准确度。
因此，为了提高准确度，我尝试使用keras.wrappers.scikit_learn中的KerasClassifier。使用带有GridSearchCV的分类器我正在考虑找到最佳参数但是在运行代码时，第一次迭代会很好，但会在下一次迭代时抛出错误。
以下是代码：
# This is the normal CNN model without GridSearch
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import numpy as np
batch_size = 128
num_classes = 2
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#Only look at 3s and 8s
train_picks = np.logical_or(y_train==2,y_train==7)
test_picks = np.logical_or(y_test==2,y_test==7)
x_train = x_train[train_picks]
x_test = x_test[test_picks]
y_train = np.array(y_train[train_picks]==7,dtype=int)
y_test = np.array(y_test[test_picks]==7,dtype=int)
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(4, kernel_size=(3, 3),activation='relu',input_shape=input_shape))
model.add(Conv2D(8, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(16, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# Improving the accuracy using GridSearch
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
def build_model(optimizer):
print(optimizer,batch_size,epochs)
model = Sequential()
model.add(Conv2D(4, kernel_size=(3, 3),activation='relu',input_shape=input_shape))
model.add(Conv2D(8, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(16, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=optimizer,
metrics=['accuracy'])
return model
model = KerasClassifier(build_fn = build_model)
parameters = {'batch_size': [128, 256],
'epochs': [10, 20],
'optimizer': ['rmsprop']}
grid_search = GridSearchCV(estimator = model,
param_grid = parameters,
scoring = 'accuracy',
cv = 10)
grid_search = grid_search.fit(x_train, y_train)
best_parameters = grid_search.best_params_
best_accuracy = grid_search.best_score_
这是代码的输出：
rmsprop 128 12
Epoch 1/10
11000/11000 [==============================] - 3s - loss: 0.1654 - acc: 0.9476
Epoch 2/10
11000/11000 [==============================] - 3s - loss: 0.0699 - acc: 0.9786
Epoch 3/10
11000/11000 [==============================] - 2s - loss: 0.0557 - acc: 0.9839
Epoch 4/10
11000/11000 [==============================] - 2s - loss: 0.0510 - acc: 0.9839
Epoch 5/10
11000/11000 [==============================] - 2s - loss: 0.0471 - acc: 0.9853
Epoch 6/10
11000/11000 [==============================] - 2s - loss: 0.0417 - acc: 0.9875
Epoch 7/10
11000/11000 [==============================] - 2s - loss: 0.0399 - acc: 0.9870
Epoch 8/10
11000/11000 [==============================] - 2s - loss: 0.0365 - acc: 0.9885
Epoch 9/10
11000/11000 [==============================] - 2s - loss: 0.0342 - acc: 0.9899
Epoch 10/10
11000/11000 [==============================] - 2s - loss: 0.0321 - acc: 0.9903
768/1223 [=================>............] - ETA: 0sTraceback (most recent call last):
File "<ipython-input-4-975b20661114>", line 30, in <module>
grid_search = grid_search.fit(x_train, y_train)
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 945, in fit
return self._fit(X, y, groups, ParameterGrid(self.param_grid))
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 564, in _fit
for parameters in parameter_iterable
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 758, in __call__
while self.dispatch_one_batch(iterator):
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 608, in dispatch_one_batch
self._dispatch(tasks)
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 571, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 109, in apply_async
result = ImmediateResult(func)
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 326, in __init__
self.results = batch()
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 260, in _fit_and_score
test_score = _score(estimator, X_test, y_test, scorer)
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 288, in _score
score = scorer(estimator, X_test, y_test)
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/metrics/scorer.py", line 98, in __call__
**self._kwargs)
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py", line 172, in accuracy_score
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "/home/thakkar_/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py", line 82, in _check_targets
"".format(type_true, type_pred))
ValueError: Can't handle mix of multilabel-indicator and binary
请帮忙！
答案 0 :(得分：0)
错误似乎与解析字典参数有关..
来自here的示例：
$stringJSON = mb_convert_encoding( $response, "UTF-8" );
echo $stringJSON; // make sure this contains valid JSON data
// stripslashes() should not be needed
var_dump( json_decode( $stringJSON ) ); // if JSON data was valid, you should get a valid PHP data structure
GridseachCV基本上从字典中获取与其输入参数匹配的元素，并训练它。您正在解析完整的字典，但import numpy
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.optimizers import SGD
# Function to create model, required for KerasClassifier
def create_model(learn_rate=0.01, momentum=0):
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
optimizer = SGD(lr=learn_rate, momentum=momentum)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = KerasClassifier(build_fn=create_model, epochs=100, batch_size=10, verbose=0)
# define the grid search parameters
learn_rate = [0.001, 0.01, 0.1, 0.2, 0.3]
momentum = [0.0, 0.2, 0.4, 0.6, 0.8, 0.9]
param_grid = dict(learn_rate=learn_rate, momentum=momentum)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)
grid_result = grid.fit(X, Y)
# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
和batch_size不是函数中的参数...
epochs也许这样的事情会起作用..没有测试过。