针对Keras回归的GridSearch实现

试图理解并实现Keras回归的GridSearch方法 . 这是我简单的可生成回归应用程序 .

import pandas as pd
import numpy as np
import sklearn
from sklearn.model_selection import train_test_split
from sklearn import metrics
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint


df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/slump/slump_test.data")
df.drop(['No','FLOW(cm)','Compressive Strength (28-day)(Mpa)'],1,inplace=True)

# Convert a Pandas dataframe to the x,y inputs that TensorFlow needs
def to_xy(df, target):
    result = []
    for x in df.columns:
        if x != target:
            result.append(x)
    # find out the type of the target column.  Is it really this hard? :(
    target_type = df[target].dtypes
    target_type = target_type[0] if hasattr(target_type, '__iter__') else target_type
    # Encode to int for classification, float otherwise. TensorFlow likes 32 bits.
    if target_type in (np.int64, np.int32):
        # Classification
        dummies = pd.get_dummies(df[target])
        return df.as_matrix(result).astype(np.float32), dummies.as_matrix().astype(np.float32)
    else:
        # Regression
        return df.as_matrix(result).astype(np.float32), df.as_matrix([target]).astype(np.float32)

x,y = to_xy(df,'SLUMP(cm)')


x_train, x_test, y_train, y_test = train_test_split(    
    x, y, test_size=0.25, random_state=42)


#Create Model
model = Sequential()
model.add(Dense(128, input_dim=x.shape[1], activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

monitor = EarlyStopping(monitor='val_loss', min_delta=1e-5, patience=5, mode='auto')
checkpointer = ModelCheckpoint(filepath="best_weights.hdf5",save_best_only=True) # save best model

model.fit(x_train,y_train,callbacks=[monitor,checkpointer],verbose=0,epochs=1000)
#model.fit(x_train,y_train,validation_data=(x_test,y_test),callbacks=[monitor,checkpointer],verbose=0,epochs=1000)
pred = model.predict(x_test)

score = np.sqrt(metrics.mean_squared_error(pred,y_test))

print("(RMSE): {}".format(score))

如果你运行代码，你可以看到 loss 不是太大的数字 .

这是我可 生产环境 的GridSearch实现 . 首先，我只是在网上搜索并找到 KerasClassifier 的GridSearch应用程序，然后尝试修改它为 KerasRegressor . 我不确定我的修订是否正确 . 如果我假设一般概念是正确的，那么这段代码一定存在问题，因为损失函数没有意义 . 损失函数是 MSE 但输出是负数，遗憾的是我无法弄清楚我做错了什么 .

from keras.wrappers.scikit_learn import KerasRegressor
import pandas as pd
import numpy as np
import sklearn
from sklearn.model_selection import train_test_split
from sklearn import metrics
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from sklearn.model_selection import GridSearchCV

df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/slump/slump_test.data")
df.drop(['No','FLOW(cm)','Compressive Strength (28-day)(Mpa)'],1,inplace=True)

#Convert a Pandas dataframe to the x,y inputs that TensorFlow needs
def to_xy(df, target):
    result = []
    for x in df.columns:
        if x != target:
            result.append(x)
    # find out the type of the target column.  Is it really this hard? :(
    target_type = df[target].dtypes
    target_type = target_type[0] if hasattr(target_type, '__iter__') else target_type
    # Encode to int for classification, float otherwise. TensorFlow likes 32 bits.
    if target_type in (np.int64, np.int32):
        #Classification
        dummies = pd.get_dummies(df[target])
        return df.as_matrix(result).astype(np.float32), dummies.as_matrix().astype(np.float32)
    else:
        #Regression
        return df.as_matrix(result).astype(np.float32), df.as_matrix([target]).astype(np.float32)

x,y = to_xy(df,'SLUMP(cm)')


x_train, x_test, y_train, y_test = train_test_split(    
    x, y, test_size=0.25, random_state=42)


def create_model(optimizer='adam'):
    # create model
    model = Sequential()
    model.add(Dense(128, input_dim=x.shape[1], activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer=optimizer,metrics=['mse'])
    return model

model = KerasRegressor(build_fn=create_model, epochs=100, batch_size=10, verbose=0)

optimizer = ['SGD', 'RMSprop', 'Adagrad']
param_grid = dict(optimizer=optimizer)

grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=1)
grid_result = grid.fit(x_train, y_train)

#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))