本文整理汇总了Python中keras.engine.training.Model.evaluate_generator方法的典型用法代码示例。如果您正苦于以下问题：Python Model.evaluate_generator方法的具体用法？Python Model.evaluate_generator怎么用？Python Model.evaluate_generator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.engine.training.Model的用法示例。
在下文中一共展示了Model.evaluate_generator方法的3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: test_model_methods
# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate_generator [as 别名]
#.........这里部分代码省略.........
yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
[np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])
out = model.fit_generator(gen_data(4), steps_per_epoch=3, epochs=5,
initial_epoch=2, callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test with a custom metric function
def mse(y_true, y_pred):
return K.mean(K.pow(y_true - y_pred, 2))
model.compile(optimizer, loss, metrics=[mse],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out_len = 1 + 2 * (1 + 1) # total loss + 2 outputs * (loss + metric)
assert len(out) == out_len
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == out_len
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4, epochs=1)
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# enable verbose for evaluate_generator
out = model.evaluate_generator(gen_data(4), steps=3, verbose=1)
# empty batch
with pytest.raises(ValueError):
def gen_data():
while True:
yield (np.asarray([]), np.asarray([]))
out = model.evaluate_generator(gen_data(), steps=1)
# x is not a list of numpy arrays.
with pytest.raises(ValueError):
out = model.predict([None])
# x does not match _feed_input_names.
with pytest.raises(ValueError):
out = model.predict([input_a_np, None, input_b_np])
with pytest.raises(ValueError):
out = model.predict([None, input_a_np, input_b_np])
# all input/output/weight arrays should have the same number of samples.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np[:2]],
[output_a_np, output_b_np],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np[:2]],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=[sample_weight[1], sample_weight[1][:2]])
示例2: test_model_methods
# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate_generator [as 别名]
#.........这里部分代码省略.........
initial_epoch=2, callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test with a custom metric function
def mse(y_true, y_pred):
return K.mean(K.pow(y_true - y_pred, 2))
model.compile(optimizer, loss, metrics=[mse],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out_len = 1 + 2 * (1 + 1) # total loss + 2 outputs * (loss + metric)
assert len(out) == out_len
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == out_len
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4, epochs=1)
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# empty batch
with pytest.raises(ValueError):
def gen_data():
while True:
yield (np.asarray([]), np.asarray([]))
out = model.evaluate_generator(gen_data(), steps=1)
# x is not a list of numpy arrays.
with pytest.raises(ValueError):
out = model.predict([None])
# x does not match _feed_input_names.
with pytest.raises(ValueError):
out = model.predict([input_a_np, None, input_b_np])
with pytest.raises(ValueError):
out = model.predict([None, input_a_np, input_b_np])
# all input/output/weight arrays should have the same number of samples.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np[:2]],
[output_a_np, output_b_np],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np[:2]],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=[sample_weight[1], sample_weight[1][:2]])
# `sample_weight` is neither a dict nor a list.
with pytest.raises(TypeError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=tuple(sample_weight))
# `validation_data` is neither a tuple nor a triple.示例3: test_model_with_external_loss
# 需要导入模块: from keras.engine.training import Model [as 别名]
# 或者: from keras.engine.training.Model import evaluate_generator [as 别名]
def test_model_with_external_loss():
# None loss, only regularization loss.
a = Input(shape=(3,), name='input_a')
a_2 = Dense(4, name='dense_1',
kernel_regularizer='l1',
bias_regularizer='l2')(a)
dp = Dropout(0.5, name='dropout')
a_3 = dp(a_2)
model = Model(a, [a_2, a_3])
optimizer = 'rmsprop'
loss = None
model.compile(optimizer, loss, metrics=['mae'])
input_a_np = np.random.random((10, 3))
# test train_on_batch
out = model.train_on_batch(input_a_np, None)
out = model.test_on_batch(input_a_np, None)
# fit
out = model.fit(input_a_np, None)
# evaluate
out = model.evaluate(input_a_np, None)
# No dropout, external loss.
a = Input(shape=(3,), name='input_a')
a_2 = Dense(4, name='dense_1')(a)
a_3 = Dense(4, name='dense_2')(a)
model = Model(a, [a_2, a_3])
model.add_loss(K.mean(a_3 + a_2))
optimizer = 'rmsprop'
loss = None
model.compile(optimizer, loss, metrics=['mae'])
# test train_on_batch
out = model.train_on_batch(input_a_np, None)
out = model.test_on_batch(input_a_np, None)
# fit
out = model.fit(input_a_np, None)
# evaluate
out = model.evaluate(input_a_np, None)
# Test fit with no external data at all.
if K.backend() == 'tensorflow':
import tensorflow as tf
a = Input(tensor=tf.Variable(input_a_np, dtype=tf.float32))
a_2 = Dense(4, name='dense_1')(a)
a_2 = Dropout(0.5, name='dropout')(a_2)
model = Model(a, a_2)
model.add_loss(K.mean(a_2))
model.compile(optimizer='rmsprop',
loss=None,
metrics=['mean_squared_error'])
# test train_on_batch
out = model.train_on_batch(None, None)
out = model.test_on_batch(None, None)
out = model.predict_on_batch(None)
# test fit
with pytest.raises(ValueError):
out = model.fit(None, None, epochs=1, batch_size=10)
out = model.fit(None, None, epochs=1, steps_per_epoch=1)
# define a generator to produce x=None and y=None
def data_tensors_generator():
while True:
yield (None, None)
generator = data_tensors_generator()
# test fit_generator for framework-native data tensors
out = model.fit_generator(generator, epochs=1,
steps_per_epoch=3)
# test evaluate_generator for framework-native data tensors
out = model.evaluate_generator(generator, steps=3)
# test fit with validation data
with pytest.raises(ValueError):
out = model.fit(None, None,
epochs=1,
steps_per_epoch=None,
validation_steps=2)
out = model.fit(None, None,
epochs=1,
steps_per_epoch=2,
validation_steps=2)
# test evaluate
with pytest.raises(ValueError):
out = model.evaluate(None, None, batch_size=10)
out = model.evaluate(None, None, steps=3)
# test predict
#.........这里部分代码省略.........