本文整理汇总了Python中deepchem.models.TensorGraph.save方法的典型用法代码示例。如果您正苦于以下问题：Python TensorGraph.save方法的具体用法？Python TensorGraph.save怎么用？Python TensorGraph.save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deepchem.models.TensorGraph的用法示例。
在下文中一共展示了TensorGraph.save方法的15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_Conv1D_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_Conv1D_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1, 1))
conv = Conv1D(2, 1, in_layers=feature)
tg.add_output(conv)
tg.set_loss(conv)
tg.build()
tg.save()示例2: test_Reshape_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_Reshape_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1))
layer = Reshape(shape=(None, 2), in_layers=feature)
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例3: test_Repeat_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_Repeat_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1))
layer = Repeat(n_times=10, in_layers=feature)
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例4: test_Exp_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_Exp_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1))
layer = Exp(feature)
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例5: test_Dense_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_Dense_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1))
dense = Dense(out_channels=1, in_layers=feature)
tg.add_output(dense)
tg.set_loss(dense)
tg.build()
tg.save()示例6: test_StopGradient_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_StopGradient_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1))
output = StopGradient(feature)
tg.add_output(output)
tg.set_loss(output)
tg.build()
tg.save()示例7: test_DTNNEmbedding_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_DTNNEmbedding_pickle():
tg = TensorGraph()
atom_numbers = Feature(shape=(None, 23), dtype=tf.int32)
Embedding = DTNNEmbedding(in_layers=[atom_numbers])
tg.add_output(Embedding)
tg.set_loss(Embedding)
tg.build()
tg.save()示例8: test_Gather_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_Gather_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1))
layer = Gather(indices=[[0], [2], [3]], in_layers=feature)
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例9: test_BatchNorm_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_BatchNorm_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 10))
layer = BatchNorm(in_layers=feature)
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例10: test_WeightedError_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_WeightedError_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 10))
layer = WeightedError(in_layers=[feature, feature])
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例11: test_Conv3DTranspose_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_Conv3DTranspose_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 10, 10, 10, 1))
layer = Conv3DTranspose(num_outputs=3, in_layers=feature)
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例12: test_ReduceSquareDifference_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_ReduceSquareDifference_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1))
layer = ReduceSquareDifference(in_layers=[feature, feature])
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例13: test_ToFloat_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_ToFloat_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 1))
layer = ToFloat(in_layers=[feature])
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例14: test_LSTM_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_LSTM_pickle():
tg = TensorGraph()
feature = Feature(shape=(tg.batch_size, 10, 10))
layer = LSTM(n_hidden=10, batch_size=tg.batch_size, in_layers=feature)
tg.add_output(layer)
tg.set_loss(layer)
tg.build()
tg.save()示例15: test_DTNNExtract_pickle
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import save [as 别名]
def test_DTNNExtract_pickle():
tg = TensorGraph()
atom_features = Feature(shape=(None, 30))
Ext = DTNNExtract(0, in_layers=[atom_features])
tg.add_output(Ext)
tg.set_loss(Ext)
tg.build()
tg.save()