本文整理汇总了Python中tensorflow.keras.Model方法的典型用法代码示例。如果您正苦于以下问题:Python keras.Model方法的具体用法?Python keras.Model怎么用?Python keras.Model使用的例子?那么, 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tensorflow.keras
的用法示例。
在下文中一共展示了keras.Model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: YoloConv
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def YoloConv(filters, name=None):
def yolo_conv(x_in):
if isinstance(x_in, tuple):
inputs = Input(x_in[0].shape[1:]), Input(x_in[1].shape[1:])
x, x_skip = inputs
# concat with skip connection
x = DarknetConv(x, filters, 1)
x = UpSampling2D(2)(x)
x = Concatenate()([x, x_skip])
else:
x = inputs = Input(x_in.shape[1:])
x = DarknetConv(x, filters, 1)
x = DarknetConv(x, filters * 2, 3)
x = DarknetConv(x, filters, 1)
x = DarknetConv(x, filters * 2, 3)
x = DarknetConv(x, filters, 1)
return Model(inputs, x, name=name)(x_in)
return yolo_conv
示例2: YoloConvTiny
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def YoloConvTiny(filters, name=None):
def yolo_conv(x_in):
if isinstance(x_in, tuple):
inputs = Input(x_in[0].shape[1:]), Input(x_in[1].shape[1:])
x, x_skip = inputs
# concat with skip connection
x = DarknetConv(x, filters, 1)
x = UpSampling2D(2)(x)
x = Concatenate()([x, x_skip])
else:
x = inputs = Input(x_in.shape[1:])
x = DarknetConv(x, filters, 1)
return Model(inputs, x, name=name)(x_in)
return yolo_conv
示例3: YoloV3Tiny
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def YoloV3Tiny(size=None,
num_channels=3,
anchors=yolo_tiny_anchors,
masks=yolo_tiny_anchor_masks,
num_classes=10,
training=False):
x = inputs = Input([*size, num_channels])
x_8, x = DarknetTiny(name='yolo_darknet', num_channels=num_channels)(x)
x = YoloConvTiny(128, name='yolo_conv_0')(x)
output_0 = YoloOutput(128, len(masks[0]), num_classes, name='yolo_output_0')(x)
x = YoloConvTiny(64, name='yolo_conv_1')((x, x_8))
output_1 = YoloOutput(64, len(masks[1]), num_classes, name='yolo_output_1')(x)
if training:
return Model(inputs, (output_0, output_1), name='yolov3')
boxes_0 = Lambda(lambda x: yolo_boxes(x, anchors[masks[0]], num_classes), name='yolo_boxes_0')(output_0)
boxes_1 = Lambda(lambda x: yolo_boxes(x, anchors[masks[1]], num_classes), name='yolo_boxes_1')(output_1)
outputs = Lambda(lambda x: yolo_nms(x, anchors, masks, num_classes), name='yolo_nms')((boxes_0[:3], boxes_1[:3]))
return Model(inputs, outputs, name='yolov3_tiny')
示例4: __call__
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def __call__(self, model):
"""
:param model: Keras model to be accelerated
:type model: Union[keras.Model, keras.Sequential]
:return: Accelerated Keras model
:rtype: Union[keras.Model, keras.Sequential]
"""
if isinstance(model, tfk.Model) or isinstance(model, tfk.Sequential):
self.model = model
else:
raise TypeError(f'FastMCInference expects tensorflow.keras Model, you gave {type(model)}')
new_input = tfk.layers.Input(shape=(self.model.input_shape[1:]), name='input')
mc_model = tfk.models.Model(inputs=self.model.inputs, outputs=self.model.outputs)
mc = FastMCInferenceMeanVar()(tfk.layers.TimeDistributed(mc_model)(FastMCRepeat(self.n)(new_input)))
new_mc_model = tfk.models.Model(inputs=new_input, outputs=mc)
return new_mc_model
示例5: build_model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def build_model(self, bert_params):
l_bert = bert.BertModelLayer.from_params(bert_params, name="bert")
l_input_ids = keras.layers.Input(shape=(128,), dtype='int32', name="input_ids")
l_token_type_ids = keras.layers.Input(shape=(128,), dtype='int32', name="token_type_ids")
output = l_bert([l_input_ids, l_token_type_ids])
output = keras.layers.Lambda(lambda x: x[:, 0, :])(output)
output = keras.layers.Dense(2)(output)
model = keras.Model(inputs=[l_input_ids, l_token_type_ids], outputs=output)
model.build(input_shape=(None, 128))
model.compile(optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy(name="acc")])
for weight in l_bert.weights:
print(weight.name)
return model, l_bert
示例6: _test_single_mode
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def _test_single_mode(layer, **kwargs):
sparse = kwargs.pop('sparse', False)
A_in = Input(shape=(None,), sparse=sparse)
X_in = Input(shape=(F,))
inputs = [X_in, A_in]
if sparse:
input_data = [X, sp_matrix_to_sp_tensor(A)]
else:
input_data = [X, A]
if kwargs.pop('edges', None):
E_in = Input(shape=(S, ))
inputs.append(E_in)
input_data.append(E_single)
layer_instance = layer(**kwargs)
output = layer_instance(inputs)
model = Model(inputs, output)
output = model(input_data)
assert output.shape == (N, kwargs['channels'])
示例7: _test_batch_mode
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def _test_batch_mode(layer, **kwargs):
A_batch = np.stack([A] * batch_size)
X_batch = np.stack([X] * batch_size)
A_in = Input(shape=(N, N))
X_in = Input(shape=(N, F))
inputs = [X_in, A_in]
input_data = [X_batch, A_batch]
if kwargs.pop('edges', None):
E_batch = np.stack([E] * batch_size)
E_in = Input(shape=(N, N, S))
inputs.append(E_in)
input_data.append(E_batch)
layer_instance = layer(**kwargs)
output = layer_instance(inputs)
model = Model(inputs, output)
output = model(input_data)
assert output.shape == (batch_size, N, kwargs['channels'])
示例8: _test_mixed_mode
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def _test_mixed_mode(layer, **kwargs):
sparse = kwargs.pop('sparse', False)
X_batch = np.stack([X] * batch_size)
A_in = Input(shape=(N,), sparse=sparse)
X_in = Input(shape=(N, F))
inputs = [X_in, A_in]
if sparse:
input_data = [X_batch, sp_matrix_to_sp_tensor(A)]
else:
input_data = [X_batch, A]
layer_instance = layer(**kwargs)
output = layer_instance(inputs)
model = Model(inputs, output)
output = model(input_data)
assert output.shape == (batch_size, N, kwargs['channels'])
示例9: get_data_and_model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def get_data_and_model(optimizer="moving_avg"):
x = tf.random.normal([TRAIN_SAMPLES, INPUT_DIM])
y = tf.random.normal([TRAIN_SAMPLES, NUM_CLASSES])
moving_avg = MovingAverage(
tf.keras.optimizers.SGD(lr=2.0), sequential_update=True, average_decay=0.5
)
if optimizer == "moving_avg":
optimizer = moving_avg
inputs = keras.layers.Input(INPUT_DIM)
hidden_layer = keras.layers.Dense(
NUM_HIDDEN, input_dim=INPUT_DIM, activation="relu"
)(inputs)
outputs = keras.layers.Dense(NUM_CLASSES, activation="softmax")(hidden_layer)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["acc"])
return x, y, model
示例10: __init__
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def __init__(self,
model: Model,
tasks: List[Task],
optimizer: str = 'adam',
learning_rate: Union[float, Callable[[int], float]] = 1e-3,
gradient_clipping: str = 'norm',
gradient_clipping_bounds: Union[float, Tuple[float, float]] = 1.,
return_loss_summaries: bool = False,
return_variable_summaries: bool = False,
return_grad_summaries: bool = False,
distribution_strategy: Type[DistributionStrategy] = MirroredStrategy,
use_memory_saving_gradients: bool = False) -> None:
learning_rate_func = PiecewiseSchedule(
[(0, 1e-6),
(100, 1e-4),
(1000, learning_rate)],
outside_value=learning_rate)
super().__init__(
model, optimizer, learning_rate_func.value, gradient_clipping, gradient_clipping_bounds,
return_loss_summaries, return_variable_summaries, return_grad_summaries,
distribution_strategy(), use_memory_saving_gradients)
self._tasks = tasks
示例11: create_model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def create_model(trainable=False):
model = MobileNetV2(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), include_top=False, alpha=ALPHA)
# to freeze layers
for layer in model.layers:
layer.trainable = trainable
out = model.layers[-1].output
x = Conv2D(4, kernel_size=3)(out)
x = Reshape((4,), name="coords")(x)
y = GlobalAveragePooling2D()(out)
y = Dense(CLASSES, name="classes", activation="softmax")(y)
return Model(inputs=model.input, outputs=[x, y])
示例12: create_model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def create_model(trainable=False):
model = MobileNetV2(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3), include_top=False, alpha=ALPHA, weights="imagenet")
for layer in model.layers:
layer.trainable = trainable
block = model.get_layer("block_16_project_BN").output
x = Conv2D(112, padding="same", kernel_size=3, strides=1, activation="relu")(block)
x = Conv2D(112, padding="same", kernel_size=3, strides=1, use_bias=False)(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
x = Conv2D(5, padding="same", kernel_size=1, activation="sigmoid")(x)
model = Model(inputs=model.input, outputs=x)
# divide by 2 since d/dweight learning_rate * weight^2 = 2 * learning_rate * weight
# see https://arxiv.org/pdf/1711.05101.pdf
regularizer = l2(WEIGHT_DECAY / 2)
for weight in model.trainable_weights:
with tf.keras.backend.name_scope("weight_regularizer"):
model.add_loss(regularizer(weight)) # in tf2.0: lambda: regularizer(weight)
return model
示例13: hybrid_model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def hybrid_model():
"""hybrid model that mixes qkeras and keras layers."""
x = x_in = keras.layers.Input((784,), name="input")
x = keras.layers.Dense(300, name="d0")(x)
x = keras.layers.Activation("relu", name="d0_act")(x)
x = QDense(100, kernel_quantizer=quantizers.quantized_bits(4, 0, 1),
bias_quantizer=quantizers.quantized_bits(4, 0, 1),
name="d1")(x)
x = QActivation("quantized_relu(4,0)", name="d1_qr4")(x)
x = QDense(
10, kernel_quantizer=quantizers.quantized_bits(4, 0, 1),
bias_quantizer=quantizers.quantized_bits(4, 0, 1),
name="d2")(x)
x = keras.layers.Activation("softmax", name="softmax")(x)
return keras.Model(inputs=[x_in], outputs=[x])
示例14: hybrid_model
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def hybrid_model():
"""hybrid model that mixes qkeras and keras layers."""
x = x_in = keras.layers.Input((784,), name="input")
x = keras.layers.Dense(300, name="d0")(x)
x = keras.layers.Activation("relu", name="d0_act")(x)
x = QDense(100, kernel_quantizer=quantizers.quantized_po2(4),
bias_quantizer=quantizers.quantized_po2(4),
name="d1")(x)
x = QActivation("quantized_relu(4,0)", name="d1_qr4")(x)
x = QDense(
10, kernel_quantizer=quantizers.quantized_po2(4),
bias_quantizer=quantizers.quantized_po2(4),
name="d2")(x)
x = keras.layers.Activation("softmax", name="softmax")(x)
return keras.Model(inputs=[x_in], outputs=[x])
示例15: qdense_model_fork
# 需要导入模块: from tensorflow import keras [as 别名]
# 或者: from tensorflow.keras import Model [as 别名]
def qdense_model_fork():
x = x_in = keras.layers.Input((23,), name="input")
x = QDense(
10,
kernel_quantizer=quantizers.quantized_bits(5, 0, 1),
bias_quantizer=quantizers.quantized_bits(5, 0, 1),
activation=quantizers.quantized_po2(3, 1),
name="qdense_0")(x)
x = QDense(
20,
kernel_quantizer=quantizers.quantized_bits(5, 0, 1),
bias_quantizer=quantizers.quantized_bits(5, 0, 1),
activation=quantizers.quantized_relu(6, 2),
name="qdense_1")(x)
x = QActivation("quantized_relu(4)", name="QA_2")(x)
x_1 = QDense(
30,
kernel_quantizer=quantizers.binary(),
bias_quantizer=quantizers.binary(),
name="qdense_3")(x)
x_2 = QActivation("quantized_relu(6,2)", name="QA_3")(x)
model = keras.Model(
inputs=[x_in], outputs=[x_1, x_2,])
return model