def test_fixed_loss_scaling(self,
strategy_fn,
experimental_run_tf_function=True):
# Note: We do not test mixed precision in this method, only loss scaling.
if not self._is_strategy_supported(strategy_fn):
return
loss_scale = 8.
batch_size = 4
with strategy_fn().scope():
x = layers.Input(shape=(1, ), batch_size=batch_size)
layer = AddLayer()
y = layer(x)
# The gradient of 'y' at this point is 1. With loss scaling, the gradient
# is 'loss_scale'. We divide by the batch size since the loss is averaged
# across batch elements.
expected_gradient = loss_scale / batch_size
identity_with_grad_check_fn = (
mp_test_util.create_identity_with_grad_check_fn(
[expected_gradient]))
y = core.Lambda(identity_with_grad_check_fn)(y)
model = models.Model(inputs=x, outputs=y)
def loss_fn(y_true, y_pred):
del y_true
return math_ops.reduce_mean(y_pred)
opt = gradient_descent.SGD(1.)
opt = loss_scale_optimizer.LossScaleOptimizer(opt, loss_scale)
model.compile(opt,
loss=loss_fn,
run_eagerly=testing_utils.should_run_eagerly(),
experimental_run_tf_function=testing_utils.
should_run_tf_function())
self.assertEqual(backend.eval(layer.v), 1)
x = np.ones((batch_size, 1))
y = np.ones((batch_size, 1))
dataset = dataset_ops.Dataset.from_tensor_slices(
(x, y)).batch(batch_size)
model.fit(dataset)
# Variable starts at 1, and should have gradient of 1 subtracted from it.
expected = 0
self.assertEqual(backend.eval(layer.v), expected)
def ternaus_model_building(img_shape):
"""Ternaus model declaration"""
inputs = layers.Input(shape=img_shape)
model = VGG16(weights="imagenet", include_top=False, input_tensor=inputs)
layer_dict = dict([(layer.name, layer) for layer in model.layers])
for key in layer_dict:
print(key)
print(layer_dict[key])
print(layer_dict[key].output_shape)
layer_dict[key].trainble = False
def decoder_block(input_tensor,
concat_tensor,
num_filters_a,
num_filters_b,
up_scale=2):
decoder = layers.Conv2DTranspose(num_filters_a, (3, 3),
strides=(up_scale, up_scale),
padding='same')(input_tensor)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation("relu")(decoder)
decoder = layers.concatenate([concat_tensor, decoder], axis=-1)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Conv2D(num_filters_b, (3, 3), padding='same')(decoder)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation("relu")(decoder)
return decoder
actual_inputs = layers.Conv2D(512, (3, 3), padding='same')(
layer_dict['block5_pool'].output)
actual_inputs = layers.BatchNormalization()(actual_inputs)
actual_inputs = decoder_block(actual_inputs,
layer_dict['block5_conv3'].output, 256, 512)
actual_inputs = decoder_block(actual_inputs,
layer_dict['block4_conv3'].output, 256, 512)
actual_inputs = decoder_block(actual_inputs,
layer_dict['block3_conv3'].output, 128, 256)
actual_inputs = decoder_block(actual_inputs,
layer_dict['block2_conv2'].output, 64, 128)
final = decoder_block(actual_inputs, layer_dict['block1_conv2'].output, 32,
1)
model = models.Model(inputs=[inputs], outputs=[final])
return model
def network_1b():
"""
将原文网络最后的FC层改为512个神经元
acc=0, size=, time=0
:return: model
"""
inputs = kl.Input(shape=(8, 16, 1))
bone = kl.BatchNormalization(1)(inputs) # modified: 加入BN层，极大加快收敛速度并提高准确率
bone = kl.Conv2D(filters=32,
kernel_size=(2, 1),
padding='same',
activation='relu',
strides=(1, 1))(bone)
bone = kl.MaxPool2D(pool_size=(2, 1), strides=(2, 1), padding='same')(bone)
bone = kl.Conv2D(filters=16,
kernel_size=(2, 1),
padding='same',
activation='relu',
strides=(1, 1))(bone)
bone = kl.MaxPool2D(pool_size=(2, 1), strides=(2, 1), padding='same')(bone)
bone = kl.Conv2D(filters=16,
kernel_size=(2, 1),
padding='same',
activation='relu',
strides=(1, 1))(bone)
bone = kl.Conv2D(filters=16,
kernel_size=(2, 1),
padding='same',
activation='relu',
strides=(1, 1))(bone)
bone = kl.Conv2D(filters=16,
kernel_size=(2, 1),
padding='same',
activation='relu',
strides=(1, 1))(bone)
bone = kl.Flatten()(bone)
bone = kl.Dense(units=512, activation='relu')(bone)
outputs = kl.Dense(units=6, activation='softmax')(bone)
model = km.Model(inputs=inputs, outputs=outputs)
model.summary()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def trivial(num_classes, batch_size=None, use_l2_regularizer=True):
input_shape = (224, 224, 3)
img_input = layers.Input(shape=input_shape, batch_size=batch_size)
x = img_input
if backend.image_data_format() == 'channels_first':
x = layers.Lambda(
lambda x: backend.permute_dimensions(x, (0, 3, 1, 2)),
name='transpose')(x)
bn_axis = 1
else: # channels_last
bn_axis = 3
x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(x)
x = layers.Conv2D(
64, (7, 7),
strides=(2, 2),
padding='valid',
use_bias=False,
kernel_initializer='he_normal',
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='conv1')(x)
x = layers.BatchNormalization(axis=bn_axis,
momentum=BATCH_NORM_DECAY,
epsilon=BATCH_NORM_EPSILON,
name='bn_conv1')(x)
rm_axes = [1, 2
] if backend.image_data_format() == 'channels_last' else [2, 3]
x = layers.Lambda(lambda x: backend.mean(x, rm_axes),
name='reduce_mean')(x)
x = layers.Dense(
num_classes,
kernel_initializer=initializers.RandomNormal(stddev=0.01),
kernel_regularizer=_gen_l2_regularizer(use_l2_regularizer),
bias_regularizer=_gen_l2_regularizer(use_l2_regularizer),
name='fc1000')(x)
# A softmax that is followed by the model loss must be done cannot be done
# in float16 due to numeric issues. So we pass dtype=float32.
x = layers.Activation('softmax', dtype='float32')(x)
# Create model.
return models.Model(img_input, x, name='resnet50')
def get_model(style_layers, content_layers):
# Load the model without the fully connected layers and pretrained on the imagenet dataset
vgg19 = tf.keras.applications.vgg19.VGG19(include_top=False,
weights=WEIGHTS)
# Set trainable to false as we don't need to train the network
vgg19.trainable = False
# Get output layers corresponding to style and content Layers
style_outputs = [vgg19.get_layer(layer).output for layer in style_layers]
content_outputs = [
vgg19.get_layer(layer).output for layer in content_layers
]
# Combining the output layers of Interest
model_outputs = style_outputs + content_outputs
# Build and return the model
return models.Model(vgg19.input, model_outputs)
def build_model():
input1 = layers.Input(shape=(X_unstructure.shape[1], ))
input2 = layers.Input(shape=(X_structure.shape[1], ))
input1_bn = layers.BatchNormalization()(input1)
x1_1 = layers.Dense(600, activation='tanh')(input1_bn)
input2_bn = layers.BatchNormalization()(input2)
x2_1 = layers.Dense(200, activation='tanh')(input2_bn)
x_connect = tf.concat([x1_1, x2_1], 1)
x_connect_1 = layers.Dense(600, activation='tanh')(x_connect)
x_connect_2 = layers.Dense(400, activation='tanh')(x_connect_1)
x_connect_2_d = layers.Dropout(0.3)(x_connect_2)
y_connect_ = layers.Dense(2, activation='softmax')(x_connect_2_d)
x1_2 = layers.Dense(600,
activation='tanh',
kernel_regularizer=regularizers.l2())(x1_1)
x2_2 = layers.Dense(400,
activation='tanh',
kernel_regularizer=regularizers.l2())(x2_1)
x1_2_d = layers.Dropout(0.3)(x1_2)
x2_2_d = layers.Dropout(0.3)(x2_2)
x1_3 = layers.Dense(600,
activation='tanh',
kernel_regularizer=regularizers.l2())(x1_2)
x2_3 = layers.Dense(400,
activation='tanh',
kernel_regularizer=regularizers.l2())(x2_2)
x1_3_d = layers.Dropout(0.3)(x1_3)
x2_3_d = layers.Dropout(0.3)(x2_3)
y1_ = layers.Dense(2, activation='softmax')(x1_3)
y2_ = layers.Dense(2, activation='softmax')(x2_3)
model = models.Model(inputs=[input1, input2],
outputs=[y_connect_, y1_, y2_])
sgd = optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
#sgd = optimizers.SGD()
model.compile(optimizer=sgd, loss=total_loss, metrics=['accuracy'])
return model
def plot_intermediate_outputs(self, num_layers: int = 12, images_per_row: int = 12, save: bool = False):
print('\nVisualizing the intermediate outputs of the first {} layers...'.format(num_layers))
if (save and check_existing_folder('outputs_visualization')) or not save:
# Collect the name of the layers for the plot
layer_names = [layer.name for layer in self.__model.layers[10:num_layers]]
# Extract the outputs of the layers
layer_outputs = [layer.output for layer in self.__model.layers[:num_layers]]
# Create a model that will return the given outputs on the base of the model input
activation_model = models.Model(inputs=self.__model.input, outputs=layer_outputs)
# Perform a prediction on the test image using the new model
activations = activation_model.predict(self.__img)
# Display the activations in a grid
self.__display_grid(layer_names, activations, images_per_row, save)
def get_model():
""" Creates our model with access to intermediate layers.
This function will load the VGG19 model and access the intermediate layers.
These layers will then be used to create a new model that will take input image
and return the outputs from these intermediate layers from the VGG model.
Returns:
returns a keras model that takes image inputs and outputs the style and
content intermediate layers.
"""
# Load our model. We load pretrained VGG, trained on imagenet data
vgg = keras.applications.vgg19.VGG19(include_top=False, weights='imagenet')
vgg.trainable = False
# Get output layers corresponding to style and content layers
style_outputs = [vgg.get_layer(name).output for name in style_layers]
content_outputs = [vgg.get_layer(name).output for name in content_layers]
model_outputs = style_outputs + content_outputs
# Build model
return models.Model(vgg.input, model_outputs)
def build_model():
sequences = layers.Input(shape=(MAX_LENGTH, ))
embedded = layers.Embedding(MAX_FEATURES, 64)(sequences)
x = layers.Conv1D(64, 3, activation='relu')(embedded)
x = layers.BatchNormalization()(x)
x = layers.MaxPool1D(3)(x)
x = layers.Conv1D(64, 5, activation='relu')(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPool1D(5)(x)
x = layers.Conv1D(64, 5, activation='relu')(x)
x = layers.GlobalMaxPool1D()(x)
x = layers.Flatten()(x)
x = layers.Dense(100, activation='relu')(x)
predictions = layers.Dense(1, activation='sigmoid')(x)
model = models.Model(inputs=sequences, outputs=predictions)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['binary_accuracy'])
return model
def build_generator(self):
dim = self.image_size[0]
mult = dim // 8
x = inputs = layers.Input((1, 1, self.z_dim))
x = ops.UpConv2D(dim // 2 * mult, 4, 1, 'valid')(x)
x = ops.BatchNorm()(x)
x = layers.ReLU()(x)
while mult > 1:
x = ops.UpConv2D(dim // 2 * (mult // 2))(x)
x = ops.BatchNorm()(x)
x = layers.ReLU()(x)
mult //= 2
x = ops.UpConv2D(3)(x)
x = layers.Activation('tanh')(x)
return models.Model(inputs, x, name='Generator')
def build_discriminator(self):
dim = self.image_size[0]
mult = 1
i = dim // 2
x = inputs = layers.Input((dim, dim, 3))
x = ops.Conv2D(dim // 2)(x)
x = ops.LeakyRelu()(x)
while i > 4:
x = ops.Conv2D(dim // 2 * (2 * mult))(x)
x = ops.LayerNorm(axis=[1, 2, 3])(x)
x = ops.LeakyRelu()(x)
i //= 2
mult *= 2
x = ops.Conv2D(1, 4, 1, 'valid')(x)
return models.Model(inputs, x, name='Discriminator')
def network_1c():
"""
BN+FC(512)+FC(6)
(实验证明FC512效果优于FC1024)
acc=0.96~0.99, size=841kb, time=0.03s
:return: model
"""
inputs = kl.Input(shape=(8, 16, 1))
bone = kl.BatchNormalization(1)(inputs)
bone = kl.Flatten()(bone)
bone = kl.Dense(units=512, activation='relu')(bone)
outputs = kl.Dense(units=6, activation='softmax')(bone)
model = km.Model(inputs=inputs, outputs=outputs)
model.summary()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def get_multiclass_model(depth, nclasses, optim, loss, mets, bias=None):
"""
Build a U-Net model
Parameters:
depth (int): number of training features (i.e. bands)
nclasses (int): number of output classes
optim (tf.keras.optimizer): keras optimizer
loss (tf.keras.loss): keras or custom loss function
mets (dict<tf.keras.metrics): dictionary of metrics for logits and classes. elements are lists of keras metrics
Returns:
tf.keras.model: compiled U-Net model
"""
if bias is not None:
bias = tf.keras.initializers.Constant(bias)
inputs = layers.Input(shape=[None, None, depth]) # 256
encoder0_pool, encoder0 = encoder_block(inputs, 32) # 128
encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64) # 64
encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128) # 32
encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256) # 16
encoder4_pool, encoder4 = encoder_block(encoder3_pool, 512) # 8
center = conv_block(encoder4_pool, 1024) # center
decoder4 = decoder_block(center, encoder4, 512) # 16
decoder3 = decoder_block(decoder4, encoder3, 256) # 32
decoder2 = decoder_block(decoder3, encoder2, 128) # 64
decoder1 = decoder_block(decoder2, encoder1, 64) # 128
decoder0 = decoder_block(decoder1, encoder0, 32) # 256
outputs = layers.Conv2D(nclasses, (1, 1),
activation='softmax',
name='softmax')(decoder0)
# logits = layers.Conv2D(1, (1, 1), activation='sigmoid', bias_initializer = bias, name = 'logits')(decoder0)
# logits is a probability and classes is binary. in solar, "tf.cast(tf.greater(x, 0.9)" was used to avoid too many false positives
# classes = layers.Lambda(lambda x: tf.cast(tf.greater(x, 0.5), dtype = tf.int32), name = 'classes')(logits)
# model = models.Model(inputs=[inputs], outputs=[logits, classes])
model = models.Model(inputs=[inputs], outputs=[outputs])
model.compile(
optimizer=optim,
loss={'softmax': loss},
#loss=losses.get(LOSS),
metrics=mets)
return model
def _get_keras_model(self) -> models.Model:
I = layers.Input(shape=(None, self._embedding_size),
dtype='float32',
name=base_model.TOKENS_FEATURE_KEY)
# Bidirectional GRU
H = I
for num_units in self.hparams().gru_units:
H = layers.Bidirectional(
layers.GRU(num_units, return_sequences=True))(I)
# Attention
last_gru_units = self.hparams(
).gru_units[-1] * 2 # x2 because bidirectional
A = layers.TimeDistributed(layers.Dense(self.hparams().attention_units,
activation='relu'),
input_shape=(None, last_gru_units))(H)
A = layers.TimeDistributed(layers.Dense(1))(A)
A = layers.Flatten()(A)
A = layers.Activation('softmax')(A)
# Dense
X = layers.Dot((1, 1))([H, A])
X = layers.Flatten()(X)
for num_units in self.hparams().dense_units:
X = layers.Dense(num_units, activation='relu')(X)
X = layers.Dropout(self.hparams().dropout_rate)(X)
# Outputs
outputs = []
for label in self._labels:
outputs.append(
layers.Dense(1, activation='sigmoid', name=label)(X))
model = models.Model(inputs=I, outputs=outputs)
model.compile(
optimizer=optimizers.Adam(lr=self.hparams().learning_rate),
loss='binary_crossentropy',
metrics=['binary_accuracy', super().roc_auc])
tf.logging.info(model.summary())
return model
def build_model(self):
"""Build an actor (policy) network that maps states -> actions."""
# Define input layer (states)
states = layers.Input(shape=(self.state_size, ), name='states')
# Add hidden layers
net = layers.Dense(units=32, activation='relu')(states)
net = layers.Dropout(0.8)(net)
net = layers.Dense(units=64, activation='relu')(net)
net = layers.Dropout(0.8)(net)
net = layers.Dense(units=32, activation='relu')(net)
# Try different layer sizes, activations, add batch normalization, regularizers, etc.
# Add final output layer with sigmoid activation
raw_actions = layers.Dense(units=self.action_size,
activation='sigmoid',
name='raw_actions')(net)
# Scale [0, 1] output for each action dimension to proper range
actions = layers.Lambda(lambda x:
(x * self.action_range) + self.action_low,
name='actions')(raw_actions)
# Create Keras model
self.model = models.Model(inputs=states, outputs=actions)
# Define loss function using action value (Q value) gradients
action_gradients = layers.Input(shape=(self.action_size, ))
loss = K.mean(-action_gradients * actions)
# Incorporate any additional losses here (e.g. from regularizers)
# Define optimizer and training function
optimizer = optimizers.Adam(lr=0.0001)
updates_op = optimizer.get_updates(params=self.model.trainable_weights,
loss=loss)
self.train_fn = K.function(
inputs=[self.model.input, action_gradients,
K.learning_phase()],
outputs=[],
updates=updates_op)
def test_save_weights_with_dynamic_loss_scaling(self, strategy_fn):
if not self._is_strategy_supported(strategy_fn):
return
strategy = strategy_fn()
if (isinstance(strategy, mirrored_strategy.MirroredStrategy)
and not context.executing_eagerly()):
# TODO(b/121381184): Enable running the test in this case.
return
# Create and run model.
with strategy.scope():
x = layers.Input(shape=(2, ), batch_size=2, dtype=dtypes.float32)
y = AddLayer(assert_type=dtypes.float32)(x)
model = models.Model(inputs=x, outputs=y)
loss_scale = loss_scale_module.DynamicLossScale(
initial_loss_scale=1., increment_period=2., multiplier=2.)
opt = gradient_descent.SGD(1.)
opt = loss_scale_optimizer.LossScaleOptimizer(opt, loss_scale)
model.compile(
optimizer=opt,
loss='mse',
run_eagerly=testing_utils.should_run_eagerly(),
run_distributed=testing_utils.should_run_distributed())
# Run for 3 steps (6 examples with a batch size of 2)
model.fit(np.zeros((6, 2)), np.zeros((6, 2)), batch_size=2)
self.assertEqual(backend.get_value(loss_scale()), 2)
self.assertEqual(backend.get_value(loss_scale._num_good_steps), 1)
# Save model weights.
save_prefix = os.path.join(self.get_temp_dir(), 'ckpt')
model.save_weights(save_prefix)
# Run model again for 1 step (2 examples with a batch size of 2)
model.fit(np.zeros((2, 2)), np.zeros((2, 2)), batch_size=2)
self.assertEqual(backend.get_value(loss_scale()), 4)
self.assertEqual(backend.get_value(loss_scale._num_good_steps), 0)
# Load model weights and ensure loss scale weights are restored.
model.load_weights(save_prefix)
self.assertEqual(backend.get_value(loss_scale()), 2)
self.assertEqual(backend.get_value(loss_scale._num_good_steps), 1)
def encoder_decoder_model(img_shape, reg_enc=False, reg_dec=False):
"""Simple encoding decoding model (UNet). Encodes by factor 2**4 and
decodes back.
Arguments:
img_shape: image shape as (height, width, channels)
reg_enc: regularize encoding
reg_dec: regularize decoding
Returns:
a keras model
"""
inputs = layers.Input(shape=img_shape)
# (256,256,3)
encoder0 = encoder_block(inputs, 32, pool=False, reg=reg_enc)
# (256,256,32)
encoder1 = encoder_block(encoder0, 64, reg=reg_enc)
# (128,128,64)
encoder2 = encoder_block(encoder1, 128, reg=reg_enc)
# (64,64,128)
encoder3 = encoder_block(encoder2, 256, reg=reg_enc)
# (32,32,256)
encoder4 = encoder_block(encoder3, 512, reg=reg_enc)
# (16,16,512)
center = encoder_block(encoder4, 1024, reg=reg_enc)
# (8,8,1024)
decoder4 = decoder_block(center, encoder4, 512, reg=reg_dec)
# (16,16,512)
decoder3 = decoder_block(decoder4, encoder3, 256, reg=reg_dec)
# (32,32,256)
decoder2 = decoder_block(decoder3, encoder2, 128, reg=reg_dec)
# (64,64,128)
decoder1 = decoder_block(decoder2, encoder1, 64, reg=reg_dec)
# (128,128,64)
decoder0 = decoder_block(decoder1, encoder0, 32, reg=reg_dec)
# (256,256,32)
outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)
# (256,256,1)
model = models.Model(inputs=[inputs], outputs=[outputs])
return model
def build_model(self):
if self.name == 'VGG19':
# Load our model. We load pretrained VGG, trained on imagenet data
vgg = tf.keras.applications.vgg19.VGG19(include_top=False,
weights='imagenet')
vgg.trainable = False
# Get output layers corresponding to style and content layers
style_outputs = [
vgg.get_layer(name).output for name in self.style_feat_layers
]
content_outputs = [
vgg.get_layer(name).output for name in self.content_feat_layers
]
model_outputs = style_outputs + content_outputs
# Build model
else:
print("Error: Not Support this model! Please Check!")
self.model = models.Model(vgg.input, model_outputs)
print(self.model.summary())
def get_model(img_shape):
inputs = layers.Input(shape=img_shape)
encoder0_pool, encoder0 = encoder_block(inputs, 8)
encoder1_pool, encoder1 = encoder_block(encoder0_pool, 16)
encoder2_pool, encoder2 = encoder_block(encoder1_pool, 32)
encoder3_pool, encoder3 = encoder_block(encoder2_pool, 64)
center = conv_block(encoder3_pool, 128)
decoder3 = decoder_block(center, encoder3, 64)
decoder2 = decoder_block(decoder3, encoder2, 32)
decoder1 = decoder_block(decoder2, encoder1, 16)
decoder0 = decoder_block(decoder1, encoder0, 8)
outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)
model = models.Model(inputs=[inputs], outputs=[outputs])
return model
def build_model(self) -> keras_models.Model:
input_shape = keras_layers.Input(shape=(None, None, 3))
kernel_size = (3, 3)
x = input_shape
for type_, name, filters in self.layers:
if type_ == 'conv':
x = self._get_conv_layer(filters, kernel_size, name)(x)
else:
pass
# TODO max pooling
# create model
model = keras_models.Model(input_shape, x, name='vgg19')
# load weight
weight_path = self._get_weight()
model.load_weights(weight_path)
return model
def get_model():
"""Creates a model with access to intermediate layers.
These layers will then be used to create a new model that will take the
content image and return the outputs from these intermediate layers from the
VGG model.
Returns:
A keras model that takes image inputs and outputs the style and content
intermediate layers.
"""
vgg = vgg19.VGG19(include_top=False, weights='imagenet')
vgg.trainable = False
style_outputs = [vgg.get_layer(name).output for name in style_layers]
content_outputs = [vgg.get_layer(name).output for name in content_layers]
model_outputs = style_outputs + content_outputs
return models.Model(vgg.input, model_outputs)
def test_save_weights_with_autocast_vars(self, strategy_fn, h5=False):
with strategy_fn().scope():
with policy.policy_scope('mixed_float16'):
x = layers.Input(shape=(1, ), batch_size=2)
layer = mp_test_util.MultiplyLayer(assert_type=dtypes.float16)
y = layer(x)
model = models.Model(inputs=x, outputs=y)
model.set_weights([np.array(100.)])
x = np.ones((2, 1))
self.assertAllClose(backend.get_value(model(x)), x * 100.)
suffix = '.h5' if h5 else ''
weights_file = os.path.join(self.get_temp_dir(), 'weights' + suffix)
model.save_weights(weights_file)
model.set_weights([np.array(200.)])
self.assertAllClose(backend.get_value(model(x)), x * 200.)
model.load_weights(weights_file)
self.assertAllClose(backend.get_value(model(x)), x * 100.)
self.assertEqual(model.get_weights(), [np.array(100.)])
def make_decoder_model(input_tensor=None, input_shape=(28, 28, 1)):
""" Create a decoder model
Args:
input_tensor: It's going to be tensor, which is going to be the array
input_shape: image tensor size.
Returns:
tf.keras.Model
"""
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
x = layers.Dense(7 * 7 * 64, activation=tf.nn.relu, name="den1")(img_input)
x = layers.Reshape(target_shape=(7, 7, 64), name="reshape1")(x)
x = layers.Conv2DTranspose(64, (3, 3),
strides=(2, 2),
padding="SAME",
activation=tf.nn.relu,
name="convt1")(x),
x = layers.Conv2DTranspose(32, (3, 3),
strides=(2, 2),
padding="SAME",
activation=tf.nn.relu,
name="convt2")(x),
x = layers.Conv2DTranspose(1, (3, 3),
strides=(1, 1),
padding="SAME",
activation=tf.nn.sigmoid,
name="convt3")(x),
if input_tensor is not None:
inputs = utils.get_source_inputs(input_tensor)
else:
inputs = img_input
model = models.Model(inputs, x, name='Decoder_model')
return model
def make_model(env):
original_matrix = np.genfromtxt(PATH_TO_CSV, delimiter=",", skip_header=39)
stateCnt = original_matrix.shape[1]
#input_shape = (10, stateCnt)
# dataframe1 = pd.read_csv(PATH_TO_CSV, header=None, usecols=range(0,67), skiprows=39, engine='python')
#dataframe.assign(s=dataframe.s.shift(-1)).drop(dataframe.index[-1])
#dataframe[dataframe.columns[-1]] = dataframe[dataframe.columns[-1]].shift(-1)
# dataframe1 = dataframe1[-1000:]
# dataset1 = dataframe1.values
# dataset1 = dataset1.astype('float64')
#UNCOMMENT THIS TO SCALE ACCORDING TO CURRENT DATA
# from sklearn import preprocessing
# scaler1 = preprocessing.MinMaxScaler()
# X1 = dataset1[:,0:67]
#scaler1.fit(X1)
# X1 = scaler1.transform(X1)
# input_shape = X1.reshape(-1,10,67)
#input_shape = observation_space
# input_shape = np.reshape(-1,10,67)
# num_frames = len(env.observation_space.spaces)
# height, width = env.observation_space.spaces[0].shape
# input_shape = height, width, num_frames
input_shape=(10, stateCnt)
ph_state = layers.Input(shape=input_shape)
# conv1 = layers.Conv2D(32, (8, 8), strides=(4, 4))(ph_state)
conv1 = layers.Conv1D(filters=2, kernel_size=1)(ph_state)
conv1 = layers.Activation('relu')(conv1)
# conv2 = layers.Conv2D(64, (4, 4), strides=(2, 2))(conv1)
# conv2 = layers.Activation('relu')(conv2)
# conv3 = layers.Conv2D(64, (3, 3), strides=(1, 1))(conv2)
# conv3 = layers.Activation('relu')(conv3)
conv_flat = layers.Flatten()(conv1)
feature = layers.Dense(512)(conv_flat)
feature = layers.Activation('relu')(feature)
# actor (policy) and critic (value) streams
size_logits = 4
size_value = 4
logits_init = initializers.RandomNormal(stddev=1e-3)
logits = layers.Dense(size_logits, kernel_initializer=logits_init)(feature)
value = layers.Dense(size_value)(feature)
return models.Model(inputs=ph_state, outputs=[logits, value])
def build_model(self):
"""Build a critic (value) network that maps (state, action) pairs -> Q-values."""
# Define input layers
states = layers.Input(shape=(self.state_size, ), name='states')
actions = layers.Input(shape=(self.action_size, ), name='actions')
# Add hidden layer(s) for state pathway
net_states = layers.Dense(units=32, activation='relu')(states)
net_states = layers.Dropout(0.8)(net_states)
net_states = layers.Dense(units=64, activation='relu')(net_states)
net_states = layers.Dropout(0.8)(net_states)
# Add hidden layer(s) for action pathway
net_actions = layers.Dense(units=32, activation='relu')(actions)
net_actions = layers.Dense(units=64, activation='relu')(net_actions)
# Try different layer sizes, activations, add batch normalization, regularizers, etc.
# Combine state and action pathways
net = layers.Add()([net_states, net_actions])
net = layers.Activation('relu')(net)
# Add more layers to the combined network if needed
# Add final output layer to prduce action values (Q values)
Q_values = layers.Dense(units=1, name='q_values')(net)
# Create Keras model
self.model = models.Model(inputs=[states, actions], outputs=Q_values)
# Define optimizer and compile model for training with built-in loss function
optimizer = optimizers.Adam(lr=0.0001)
self.model.compile(optimizer=optimizer, loss='mse')
# Compute action gradients (derivative of Q values w.r.t. to actions)
action_gradients = K.gradients(Q_values, actions)
# Define an additional function to fetch action gradients (to be used by actor model)
self.get_action_gradients = K.function(
inputs=[*self.model.input, K.learning_phase()],
outputs=action_gradients)
def get_model256():
inputs = layers.Input(shape=(256, 256, 3))
# 256
encoder0_pool, encoder0 = encoder_block(inputs, 32)
# 128
encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64)
# 64
encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128)
# 32
encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256)
# 16
encoder4_pool, encoder4 = encoder_block(encoder3_pool, 512)
# 8
center = conv_block(encoder4_pool, 1024)
# center
decoder4 = decoder_block(center, encoder4, 512)
# 16
decoder3 = decoder_block(decoder4, encoder3, 256)
# 32
decoder2 = decoder_block(decoder3, encoder2, 128)
# 64
decoder1 = decoder_block(decoder2, encoder1, 64)
# 128
decoder0 = decoder_block(decoder1, encoder0, 32)
# 256
outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)
model = models.Model(inputs=[inputs], outputs=[outputs])
return model
def test_model(self, strategy_fn, use_operator=False, use_regularizer=False,
cloning=True):
if not self._is_strategy_supported(strategy_fn):
return
regularizer = IdentityRegularizer() if use_regularizer else None
with strategy_fn().scope():
with policy.policy_scope('infer_float32_vars'):
x = layers.Input(shape=(1,), batch_size=2, dtype=dtypes.float16)
layer = AddLayer(assert_type=dtypes.float16, use_operator=use_operator,
regularizer=regularizer)
y = layer(x)
y = math_ops.cast(y, dtypes.float32)
model = models.Model(inputs=x, outputs=y)
def loss_fn(y_true, y_pred):
del y_true
return math_ops.reduce_mean(y_pred)
# Learning rate is small enough that if applied to a float16 variable,
# the variable will not change. So this tests the learning rate not
# applied to a float16 value, but instead the float32 variable.
opt = gradient_descent.SGD(2 ** -14)
model.compile(
opt,
loss=loss_fn,
cloning=cloning,
run_eagerly=testing_utils.should_run_eagerly(),
run_distributed=testing_utils.should_run_distributed())
self.assertEqual(backend.eval(layer.v), 1)
x = np.ones((2, 1))
y = np.ones((2, 1))
dataset = dataset_ops.Dataset.from_tensor_slices((x, y)).batch(2)
model.fit(dataset)
# Variable starts at 1, and should have gradient of 2 ** -14 subtracted
# from it.
expected = 1 - 2 ** -14
if use_regularizer:
# Regularizer adds another 2 ** -14 to the gradient.
expected -= 2 ** -14
self.assertEqual(backend.eval(layer.v), expected)
def quick_eval(params,save_model_path):
img_dir = os.path.join(params["data_dir"], "test_images")
label_dir = os.path.join(params["data_dir"], "test_labels")
ids_train = [i.replace(".png","") for i in os.listdir(img_dir)]
x_train_filenames = []
y_train_filenames = []
for img_id in ids_train:
x_train_filenames.append(os.path.join(img_dir, "{}.png".format(img_id)))
y_train_filenames.append(os.path.join(label_dir, "{}.png".format(img_id)))
y_val_filenames = y_train_filenames
x_val_filenames = x_train_filenames
num_train_examples = len(x_train_filenames)
num_val_examples = len(x_val_filenames)
print("Number of training examples: {}".format(num_train_examples))
print("Number of validation examples: {}".format(num_val_examples))
# Alternatively, load the weights directly: model.load_weights(save_model_path)
from keras.models import load_model
inputs, outputs = model_fn(params["img_shape"])
model = models.Model(inputs=[inputs], outputs=[outputs])
model.load_weights(save_model_path)
train_ds,val_ds,temp_ds = batch_data_sets(x_train_filenames,y_train_filenames,x_val_filenames,y_val_filenames)
# Let's visualize some of the outputs
data_aug_iter = val_ds.make_one_shot_iterator()
next_element = data_aug_iter.get_next()
# Running next element in our graph will produce a batch of images
test_losses = []
for i in range(12):
batch_of_imgs, label = tf.keras.backend.get_session().run(next_element)
img = batch_of_imgs[0]
loss = model.evaluate(x=batch_of_imgs, y=label, batch_size=1, verbose=1, sample_weight=None, steps=None)
test_losses.append(loss)
print("average dice score over all samples are:{}".format(np.mean(test_losses)))
def test_save_weights(self, strategy_fn, h5=False):
with strategy_fn().scope():
with policy.policy_scope('infer_float32_vars'):
x = layers.Input(shape=(), batch_size=2, dtype=dtypes.float16)
layer = AddLayer(assert_type=dtypes.float16)
y = layer(x)
y = math_ops.cast(y, dtypes.float32)
model = models.Model(inputs=x, outputs=y)
model.set_weights([np.array(100.)])
x = np.ones((2, 1), dtype=np.float16)
self.assertAllClose(backend.get_value(model(x)), x + 100.)
suffix = '.h5' if h5 else ''
weights_file = os.path.join(self.get_temp_dir(), 'weights' + suffix)
model.save_weights(weights_file)
model.set_weights([np.array(200.)])
self.assertAllClose(backend.get_value(model(x)), x + 200.)
model.load_weights(weights_file)
self.assertAllClose(backend.get_value(model(x)), x + 100.)
self.assertEqual(model.get_weights(), [np.array(100.)])
def get_model():
# 内容层选取 block5_conv2
content_layers = ['block5_conv2']
# 风格层 conv1
style_layers = ['block1_conv1',
'block2_conv1',
'block3_conv1',
'block4_conv1',
'block5_conv1']
# 导入VGG19模型，权重使用imagenet
vgg = tf.keras.applications.vgg19.VGG19(include_top=False, weights='imagenet')
vgg.trainable = False
# 提取风格特征
style_outputs = [vgg.get_layer(name).output for name in style_layers]
# 提取内容特征
content_outputs = [vgg.get_layer(name).output for name in content_layers]
# 输出结果特征
model_outputs = style_outputs + content_outputs
return models.Model(vgg.input, model_outputs)