python - 带有 Keras 分类器的“ValueError:激活不是合法参数”
问题描述
我一直在玩 Tensorflow 和 Keras,最终在尝试超参数调整时遇到以下错误:“ValueError:激活不是合法参数”
关键是我想在我的模型中尝试不同的激活函数,看看哪一个效果最好。我有以下代码:
import pandas as pd
import tensorflow as tf
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
import numpy as np
ds = pd.read_csv(
"https://storage.googleapis.com/download.tensorflow.org/data/abalone_train.csv",
names=["Length", "Diameter", "Height", "Whole weight", "Shucked weight",
"Viscera weight", "Shell weight", "Age"])
print(ds)
x_train = ds.copy()
y_train = x_train.pop('Age')
x_train = np.array(x_train)
def create_model(layers, activations):
model = tf.keras.Sequential()
for i, nodes in enumerate(layers):
if i == 0:
model.add(tf.keras.layers.Dense(nodes, input_dim=x_train.shape[1]))
model.add(layers.Activation(activations))
model.add(Dropout(0.3))
else:
model.add(tf.keras.layers.Dense(nodes))
model.add(layers.Activation(activations))
model.add(Dropout(0.3))
model.add(tf.keras.layers.Dense(units=1, kernel_initializer='glorot_uniform'))
model.add(layers.Activation('sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
model = KerasClassifier(build_fn=create_model, verbose=0)
layers = [[20], [40,20], [45, 30, 15]]
activations = ['sigmoid', 'relu']
param_grid = dict(layers=layers, activation=activations, batch_size = [128, 256], epochs=[30])
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)
grid_result = grid.fit(x_train, y_train)
print(grid_result.best_score_,grid_reslult.best_params_)
pred_y = grid.predict(x_test)
y_pred = (pred_y > 0.5)
cm=confusion_matrix(y_pred, y_test)
score=accuracy_score(y_pred, y_test)
model.fit(x_train, y_train, epochs=30, callbacks=[cp_callback])
#steps_per_epoch
model.evaluate(x_test, y_test, verbose=2)
probability_model = tf.keras.Sequential([
model,
tf.keras.layers.Softmax()
])
probability_model(x_test[:100])
解决方案
如果您在此处看到,则必须将激活指定为:
from tensorflow.keras import activations
layers.Activation(activations.relu)
现在,你有:
activations = ['sigmoid', 'relu']
所以,这就是价值错误的原因。
你应该把你的代码改成这样:
model.add(tf.keras.layers.Dense(nodes, activation=activations[i], input_dim=x_train.shape[1]))
因此,删除激活层:model.add(layers.Activation(activations))
而是在每个层内使用激活。
例子:
def create_model(layers, activations):
model = tf.keras.Sequential()
for i in range(2):
if i == 0:
model.add(tf.keras.layers.Dense(2, activation=activations[i], input_dim=x_train.shape[1]))
model.add(tf.keras.layers.Dropout(0.3))
else:
model.add(tf.keras.layers.Dense(2, activation=activations[i]))
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.Dense(units=1, activation='sigmoid', kernel_initializer='glorot_uniform'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
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