本文整理汇总了Python中keras.metrics.get方法的典型用法代码示例。如果您正苦于以下问题：Python metrics.get方法的具体用法？Python metrics.get怎么用？Python metrics.get使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.metrics的用法示例。
在下文中一共展示了metrics.get方法的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: evaluate
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def evaluate(self, inputs, fn_inverse=None, fn_plot=None):
try:
X, y = inputs
inputs = X
except:
X, conditions, y = inputs
inputs = [X, conditions]
y_hat = self.predict(inputs)
if fn_inverse is not None:
y_hat = fn_inverse(y_hat)
y = fn_inverse(y)
if fn_plot is not None:
fn_plot([y, y_hat])
results = []
for m in self.model.metrics:
if isinstance(m, str):
results.append(K.eval(K.mean(get(m)(y, y_hat))))
else:
results.append(K.eval(K.mean(m(y, y_hat))))
return results 示例2: evaluate
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def evaluate(self, data, fn_inverse=None, fn_plot=None):
try:
X, y = data
y_hat = self.predict(X)
except:
X, X_ex, y = data
y_hat = self.predict([X, X_ex])
if fn_inverse is not None:
y = fn_inverse(y)
y_hat = fn_inverse(y_hat)
y = np.float32(y)
y_hat = np.float32(y_hat)
if fn_plot is not None:
fn_plot([y, y_hat])
results = []
for m in self.model.metrics:
if isinstance(m, str):
results.append(K.eval(K.mean(get(m)(y, y_hat))))
else:
results.append(K.eval(K.mean(m(y, y_hat))))
return results 示例3: __init__
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def __init__(self,
layers,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
dropout=0.0,
recursive_forecast=False,
):
"""
Base FeedForward Neural Network.
:param layers: list of integers. The i-th elem. of the list is the number of units of the i-th layer.
:param dropout: An integer or tuple/list of a single integer, specifying the length
of the 1D convolution window.
:param recursive_forecast: an integer or tuple/list of a single integer, specifying the dilation rate
to use for dilated convolution.
Usually dilation rate increases exponentially with the depth of the network.
:param for all the other parameters see keras.layers.Dense
"""
self.layers = layers
self.dropout = dropout
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.recursive_forecast = recursive_forecast
self.model = None
self.horizon = None 示例4: _eval
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def _eval(self, y, y_hat):
results = []
for m in self.model.metrics:
if isinstance(m, str):
results.append(K.eval(K.mean(get(m)(y, y_hat))))
else:
results.append(K.eval(K.mean(m(y, y_hat))))
return results 示例5: _build_encoder
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def _build_encoder(self):
"""
Build the encoder multilayer RNN (stacked RNN)
"""
# Create a list of RNN Cells, these get stacked one after the other in the RNN,
# implementing an efficient stacked RNN
encoder_cells = []
for n_hidden_neurons in self.encoder_layers:
encoder_cells.append(self.cell(units=n_hidden_neurons,
dropout=self.dropout,
kernel_regularizer=l2(self.l2),
recurrent_regularizer=l2(self.l2)))
self.encoder = RNN(encoder_cells, return_state=True, name='encoder') 示例6: evaluate
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def evaluate(self, data, fn_inverse=None, horizon=1, fn_plot=None):
"""
Evaluate model
:return:
"""
encoder_input_data, decoder_input_exog, y = data
y_hat = self.predict(encoder_inputs=encoder_input_data,
pred_steps=horizon,
decoder_input_exog=decoder_input_exog)
if fn_inverse is not None:
y = fn_inverse(y)
y_hat = fn_inverse(y_hat)
y = np.float32(y)
y_hat = np.float32(y_hat)
if fn_plot is not None:
fn_plot([y,y_hat])
results = []
for m in self.model.metrics:
if isinstance(m, str):
results.append(K.eval(K.mean(get(m)(y, y_hat))))
else:
results.append(K.eval(K.mean(m(y, y_hat))))
return results 示例7: test_invalid_get
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def test_invalid_get():
with pytest.raises(ValueError):
metrics.get(5) 示例8: evaluate_keras_metric
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def evaluate_keras_metric(y_true, y_pred, metric):
objective_function = metrics.get(metric)
objective = objective_function(y_true, y_pred)
return K.eval(objective) 示例9: on_epoch_end
# 需要导入模块: from keras import metrics [as 别名]
# 或者: from keras.metrics import get [as 别名]
def on_epoch_end(self, batch, logs={}):
val_loss, val_acc, y_true, y_pred, y_true_class, y_pred_class = evaluate_model(self.model, self.val_gen, self.val_steps, self.metric, self.category_cutoffs)
test_loss, test_acc, _, _, _, _ = evaluate_model(self.model, self.test_gen, self.test_steps, self.metric, self.category_cutoffs)
self.progbar.append_extra_log_values([('val_acc', val_acc), ('test_loss', test_loss), ('test_acc', test_acc)])
if float(logs.get('val_loss', 0)) < self.best_val_loss:
plot_error(y_true, y_pred, batch, self.ext, self.pre)
self.best_val_loss = min(float(logs.get('val_loss', 0)), self.best_val_loss)
self.best_val_acc = max(float(logs.get('val_acc', 0)), self.best_val_acc)