本文整理汇总了Python中keras.models.Sequential._make_train_function方法的典型用法代码示例。如果您正苦于以下问题：Python Sequential._make_train_function方法的具体用法？Python Sequential._make_train_function怎么用？Python Sequential._make_train_function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.models.Sequential的用法示例。
在下文中一共展示了Sequential._make_train_function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_pickling_right_after_compilation
# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import _make_train_function [as 别名]
def test_pickling_right_after_compilation():
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
model.add(Dense(3))
model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
model._make_train_function()
model = pickle.loads(pickle.dumps(model))示例2: test_saving_right_after_compilation
# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import _make_train_function [as 别名]
def test_saving_right_after_compilation():
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
model.add(Dense(3))
model.compile(loss='mse', optimizer='sgd', metrics=['acc'])
model._make_train_function()
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
model = load_model(fname)
os.remove(fname)示例3: Adam
# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import _make_train_function [as 别名]
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
# model.add(Dense(nb_classes))
model.add(Dense(nb_classes, kernel_initializer='zero', activation=masked_softmax))
# Define our training protocol
protocol_name, protocol = protocols.PATH_INT_PROTOCOL(omega_decay='sum', xi=1e-3 )
opt = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999)
# opt = RMSprop(lr=1e-3)
# opt = SGD(1e-3)
oopt = KOOptimizer(opt, model=model, **protocol)
model.compile(loss='categorical_crossentropy', optimizer=oopt, metrics=['accuracy'])
model._make_train_function()
history = LossHistory()
callbacks = [history]
datafile_name = "split_cifar10_data_%s_lr%.2e_ep%i.pkl.gz"%(protocol_name, learning_rate, epochs_per_task)
def run_fits(cvals, training_data, valid_data, nstats=1):
acc_mean = dict()
acc_std = dict()
for cidx, cval_ in enumerate(cvals):
runs = []
for runid in range(nstats):
evals = []
sess.run(tf.global_variables_initializer())