def __init__(self, model_path, class_labels_json):
self.class_labels = class_labels_json
self.height = 150
self.width = 150
self.model_path = model_path
self.model = Sequential()
self.class_labels = self.read_class_labels_if_exists()
def _make_layer(self,
name,
block,
out_channels,
num_blocks,
stride=1,
dilate=False):
## Constructs a layer of blocks
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != out_channels * block.expansion:
downsample = Sequential([
layers.Conv2D(out_channels * block.expansion, (1, 1), stride),
layers.BatchNormalization()
])
self.inplanes = out_channels * block.expansion
blocks = [
block(out_channels=out_channels,
stride=stride,
downsample=downsample,
base_width=self.base_width,
dilation=previous_dilation)
]
for _ in range(1, num_blocks):
blocks.append(
block(out_channels=out_channels,
base_width=self.base_width,
dilation=self.dilation))
return Sequential(layers=blocks, name=name)
def make_resnet(net):
match = re.match(r'([a-z]*)(\d+)', net)
net_type, n_layers = match.group(1), match.group(2)
residual = (net_type == 'resnet')
n = (int(n_layers) - 2) // 6
print("Net: detected n = {} {} shortcuts".format(
n, 'with' if residual else 'without'))
Block = ConvBNReluResidualBlock if residual else ConvBNReluBlock
_layers = [
ConvBNRelu(strides=1, filters=16, input_shape=(32, 32, 3)),
Block(strides=1, filters=16),
[Block(strides=1, filters=16) for _ in range(n - 1)],
Block(strides=2, filters=32),
[Block(strides=1, filters=32) for _ in range(n - 1)],
Block(strides=2, filters=64),
[Block(strides=1, filters=64) for _ in range(n - 1)],
GlobalAveragePooling2D(),
Dense(10, 'softmax')
]
layers = []
for x in _layers:
try:
for l in x:
layers.append(l)
except TypeError:
layers.append(x)
model = Sequential(layers)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['acc'])
return model
def train(self):
self.model = Sequential()
self.model.add(Dense(self.n1, activation=tf.nn.relu, use_bias=self.bias, input_shape=(1,)))
if self.n2 != 0:
self.model.add(Dense(self.n2, activation=tf.nn.relu, use_bias=self.bias))
self.model.add(Dense(1))
self.model.compile(optimizer=self.optimizer, loss=self.loss, metrics=['mse', 'mse'])
self.keys, self.values = import_data(self.filename)
self.model.fit(self.keys, self.values, epochs=self.epochs, batch_size=self.batch_size, verbose=self.verbose,
validation_split=self.validation_split)
if not os.path.exists("models_tf/{}".format(self.identifier)):
os.makedirs("models_tf/{}".format(self.identifier))
self.model.save("models_tf/{}/super_layer.h5".format(self.identifier))
converter = tf.lite.TFLiteConverter.from_keras_model(self.model) # TF 2.0
# converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
# Following code is used for further optimization of weights. Here we notice a massive drop in performance
converter.optimizations = [tf.lite.Optimize.DEFAULT]
sample = tf.cast(self.keys, tf.float32)
sample = tf.data.Dataset.from_tensor_slices((sample)).batch(1)
def representative_data_gen():
for input_value in sample.take(1300000):
yield [input_value]
converter.representative_dataset = representative_data_gen
tflite_model = converter.convert()
open("models_tf/{}/super_layer.tflite".format(self.identifier), "wb").write(tflite_model)
def create_model(self):
model = Sequential()
model.add(Dense(512, activation=relu, input_dim=self.state_size))
model.add(Dense(512, activation=relu))
model.add(Dense(self.action_size, activation=softmax))
model.compile(optimizer=adam(), loss=categorical_crossentropy)
return model
def train(self):
batch_size = 64
nb_epoch = 100
if self.has_train:
nb_epoch = nb_epoch - self.epoch
print('new epoch', nb_epoch)
self.model.fit(self.X_train,
self.y_train,
batch_size=batch_size,
epochs=nb_epoch,
callbacks=[self.checkpointer, self.csv_logger])
else:
# 1. define the network
self.model = Sequential()
self.model.add(Dense(1024, input_dim=41, activation='relu'))
self.model.add(Dropout(0.01))
self.model.add(Dense(1))
self.model.add(Activation('sigmoid'))
self.model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
self.model.fit(self.X_train,
self.y_train,
batch_size=batch_size,
epochs=nb_epoch,
callbacks=[self.checkpointer, self.csv_logger])
self.model.save("./dnn1layer_model.hdf5")
score, acc = self.model.evaluate(self.X_test, self.y_test)
print('Test score:', score)
print('Test accuracy', acc)
def __init__(self, channel, reduction=16):
super().__init__()
self.fc = Sequential()
self.fc.add(layers.GlobalAveragePooling1D())
self.fc.add(layers.Dense(channel // reduction, activation='relu'))
self.fc.add(layers.Dense(channel))
self.fc.add(layers.Activation('sigmoid'))
def build_q_CNN(self):
self.model = Sequential()
self.model.add(
Conv2D(32,
kernel_size=8,
activation='relu',
strides=4,
input_shape=self.input_shape))
self.model.add(Conv2D(64, kernel_size=4, activation='relu', strides=2))
self.model.add(Conv2D(64, kernel_size=3, activation='relu', strides=1))
self.model.add(Flatten())
self.model.add(Dense(512, activation='relu'))
self.model.add(Dense(self.action_size, activation='linear'))
self.model.compile(loss=self.loss_metric,
optimizer=Adam(lr=self.learning_rate,
decay=self.learning_rate_decay))
return self.model
def make_resnet(net):
n, residual = parse_net(net)
print("Net: detected n = {} {} shortcuts".format(
n, 'with' if residual else 'without'))
Block = ConvBNReluResidualBlock if residual else ConvBNReluBlock
layers = [
ConvBNRelu(strides=1,
kernel_size=3,
filters=16,
input_shape=(32, 32, 3)),
Block(strides=1, kernel_size=3, filters=16),
[Block(strides=1, kernel_size=3, filters=16) for _ in range(n - 1)],
Block(strides=2, kernel_size=3, filters=32),
[Block(strides=1, kernel_size=3, filters=32) for _ in range(n - 1)],
Block(strides=2, kernel_size=3, filters=64),
[Block(strides=1, kernel_size=3, filters=64) for _ in range(n - 1)],
GlobalAveragePooling2D(),
Dense(10, 'softmax')
]
layers = flatten_layers(layers)
model = Sequential(layers)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['acc'])
return model
def _build_model(self):
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
return model
def build_q_dense_from_json(self):
self.model = Sequential()
len_layers = len(self.layers)
assert len_layers >= 2, 'You must have a network of \
at least 2 layers'
for i, units in enumerate(self.layers):
if i == 0:
self.model.add(
Dense(units=units,
input_dim=self.observations_size,
activation='relu',
name='Input_state'))
else:
self.model.add(
Dense(units=units, activation='relu', name='Hidden_layer'))
if i + 1 == len_layers:
break
self.model.add(
Dense(self.action_size,
activation='linear',
name='Output_Q_action'))
self.model.compile(loss=self.loss_metric,
optimizer=Adam(lr=self.learning_rate,
decay=self.learning_rate_decay))
return self.model
def get_bidirectional_model(self, pre_embeddings, dp_rate=0.0, use_lstm=False):
"""
follow the common model construction step shown in keras manual
:param pre_embeddings:
:param dp_rate: drop out rate
:param use_lstm: utilize LSTM or GRU unit
:return: the model
"""
# Embedding part can try multichannel as same as origin paper
embedding_layer = Embedding(self.max_features, # 字典长度
self.embedding_dims, # 词向量维度
weights=[pre_embeddings], # 预训练的词向量
input_length=self.maxlen, # 每句话的最大长度
trainable=False # 是否在训练过程中更新词向量
)
model = Sequential()
model.add(embedding_layer)
if use_lstm:
model.add(Bidirectional(LSTM(RNN_DIM, recurrent_dropout=dp_rate)))
else:
model.add(Bidirectional(GRU(RNN_DIM, recurrent_dropout=dp_rate)))
# model.add(Dropout(dp_rate))
model.add(Dense(self.class_num, activation=self.last_activation))
return model
def build_nn_model(input_shape,
nn_define,
loss,
optimizer,
metrics,
is_supported_layer=has_builder,
default_layer=None) -> KerasNNModel:
model = Sequential()
is_first_layer = True
for layer_config in nn_define:
layer = layer_config.get("layer", default_layer)
if layer and is_supported_layer(layer):
del layer_config["layer"]
if is_first_layer:
layer_config["input_shape"] = input_shape
is_first_layer = False
builder = get_builder(layer)
model.add(builder(**layer_config))
else:
raise ValueError(f"dnn not support layer {layer}")
return from_keras_sequential_model(model=model,
loss=loss,
optimizer=optimizer,
metrics=metrics)
def build_simple_model():
model = Sequential()
model.add(Flatten(input_shape=(lookback // step, float_data.shape[-1])))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=RMSprop(), loss='mae')
return model
class MyAlexNet(Model):
def __init__(self):
super(MyAlexNet, self).__init__()
self.Layers = [
layers.Conv2D(filters=48 , kernel_size=[3, 3], padding='same', activation=nn.relu, kernel_regularizer=regularizers.l2(hps.lamda)), # 64
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Conv2D(filters=128, kernel_size=[3, 3],padding='same', activation=nn.relu, kernel_regularizer=regularizers.l2(hps.lamda)), # 192
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Conv2D(filters=192, kernel_size=[3, 3], padding='same', activation=nn.relu, kernel_regularizer=regularizers.l2(hps.lamda)), # 384
layers.Conv2D(filters=192, kernel_size=[3, 3], padding='same', activation=nn.relu, kernel_regularizer=regularizers.l2(hps.lamda)), # 256
layers.Conv2D(filters=128, kernel_size=[3, 3], padding='same', activation=nn.relu, kernel_regularizer=regularizers.l2(hps.lamda)), # 256
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Flatten(),
layers.Dense(2048, activation=nn.relu, kernel_regularizer=regularizers.l2(hps.lamda)), # 2048
layers.Dense(2048, activation=nn.relu, kernel_regularizer=regularizers.l2(hps.lamda)), # 2048
layers.Dense(10, activation=nn.softmax, kernel_regularizer=regularizers.l2(hps.lamda)),
# layers.Dense(10, activation=None),
]
self.net = Sequential(self.Layers)
self.net.build(input_shape=[None, 32, 32, 3])
def call(self, inputs, training=None, mask=None):
inputs = tf.reshape(inputs, [-1, 32, 32, 3])
out = self.net(inputs)
return out
def downsample(units,
input_shape=None,
apply_batchnorm=True,
layer_type='dense'):
initializer = random_normal_initializer(0., 0.02)
seq = Sequential()
if layer_type == 'dense':
seq.add(
layers.Dense(units,
input_shape=[
input_shape,
],
kernel_initializer=initializer,
use_bias=False))
elif layer_type == 'conv':
seq.add(
layers.Conv2D(filters=units,
kernel_size=3,
strides=(2, 2),
padding='same',
input_shape=input_shape,
kernel_initializer=initializer,
use_bias=False))
else:
raise ValueError('wrong layer type!')
if apply_batchnorm:
seq.add(layers.BatchNormalization())
seq.add(layers.LeakyReLU())
return seq
def model_builder():
model = Sequential()
model.add(Dense(256, input_dim=len(x_train.columns), kernel_initializer = "random_normal", activation = "relu"))
model.add(Dense(256, kernel_initializer = "random_normal", activation = "relu"))
model.add(Dense(1, kernel_initializer = "random_normal", activation="relu"))
model.compile(optimizer="adam", loss="mean_squared_error")
return model
def train(self):
batch_size = 64
units = 100
embedding_matrix = np.zeros((self.vocab_size, 100))
for word, index in self.tk.word_index.items():
embedding_vector = self.word2vec.get(word)
if embedding_vector is not None:
embedding_matrix[index] = embedding_vector
self.model = Sequential()
self.model.add(
Embedding(self.vocab_size,
units,
weights=[embedding_matrix],
trainable=False))
self.model.add(
Bidirectional(LSTM(units, return_sequences=True, dropout=0.2)))
self.model.add(Bidirectional(LSTM(units, dropout=0.2)))
self.model.add(Dense(self.output_size, activation='sigmoid'))
print(self.model.summary())
self.model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['acc'])
history = self.model.fit(self.X_train,
self.y_train,
epochs=100,
batch_size=batch_size,
verbose=1)
def get_model(h1, h2, initializer="random_uniform"):
model = Sequential()
model.add(Dense(h1, activation="tanh", kernel_initializer=initializer))
if h2 != 0:
model.add(Dense(h2, activation="tanh", kernel_initializer=initializer))
model.add(Dense(1, activation="tanh", kernel_initializer=initializer))
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])
return model
def create_model(self) -> Sequential:
model = Sequential()
model.add(Dense(64, input_dim=4, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(2, activation='linear'))
model.compile(loss="mse", optimizer=Adam(self.learning_rate))
return model
def DeepQNetwork(lr, num_actions, input_dims, fc1, fc2):
q_net = Sequential()
q_net.add(Dense(fc1, input_dim=input_dims, activation='relu'))
q_net.add(Dense(fc2, activation='relu'))
q_net.add(Dense(num_actions, activation=None))
q_net.compile(optimizer=Adam(learning_rate=lr), loss='mse')
return q_net
def createModel(self):
"""Create and compile the keras model. See layers-18pct.cfg and
layers-params-18pct.cfg for the network model,
and https://code.google.com/archive/p/cuda-convnet/wikis/LayerParams.wiki
for documentation on the layer format.
"""
self.model = Sequential()
self.model.add(
keras.layers.Conv2D(filters=32,
kernel_size=5,
strides=(1, 1),
padding='same',
input_shape=(32, 32, 3),
data_format="channels_last",
dilation_rate=(1, 1),
activation=tf.nn.relu))
self.model.add(
keras.layers.MaxPooling2D(pool_size=(3, 3),
strides=(2, 2),
padding='same'))
self.model.add(
keras.layers.BatchNormalization(
axis=1,
momentum=0.99,
epsilon=0.001,
))
self.model.add(
keras.layers.Conv2D(filters=32,
kernel_size=5,
strides=(1, 1),
padding='same',
dilation_rate=(1, 1),
activation=tf.nn.relu))
self.model.add(
keras.layers.AveragePooling2D(pool_size=(3, 3),
strides=(2, 2),
padding='same'))
self.model.add(
keras.layers.BatchNormalization(axis=-1,
momentum=0.99,
epsilon=0.001))
self.model.add(
keras.layers.Conv2D(filters=32,
kernel_size=5,
strides=(1, 1),
padding='same',
dilation_rate=(1, 1),
activation=tf.nn.relu))
self.model.add(
keras.layers.AveragePooling2D(pool_size=(3, 3),
strides=(2, 2),
padding='same'))
self.model.add(keras.layers.Flatten())
self.model.add(keras.layers.Dense(10, activation=tf.nn.softmax))
self.model.compile(optimizer=keras.optimizers.Adam(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
def build_simple_rnn_model(max_features=10000):
model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['acc'])
return model
def __init__(self):
#Here we'll create the brain of our agent, a deep neural network with Keras
self.model = Sequential()
self.q_targets = []
self.model.add(kl.Dense(10, input_dim=27))
self.model.add(kl.Activation('relu'))
self.model.add(kl.Dense(4, input_dim=10))
self.model.add(kl.Activation('tanh'))
self.model.compile(optimizer="adam", loss='MSE', metrics=['accuracy'])
def __init__(self):
super(myModel, self).__init__()
"""
初始化我们自己需要 使用到的神经网络层。
"""
self.rows = None
self.cols = None
self.kernel = (3, 3)
self.init = RandomNormal(stddev=0.01)
self.model = Sequential()
def __init__(self,
discount_factor,
name='DQNetwork',
model=None,
state_size=None,
action_size=None,
hidden_sizes=None,
hidden_activation_functions=None,
output_activation_function=None):
self.name = name
self.discount_factor = discount_factor
if model is not None:
self.model = load_model(model)
else:
try:
assert state_size is not None
assert action_size is not None
assert output_activation_function is not None
except AssertionError:
print(
"Network needs to know at least state_size, actionsize, and have an output_activation_function"
)
try:
if hidden_activation_functions is not None or hidden_sizes is not None:
assert len(hidden_activation_functions) == len(
hidden_sizes)
except AssertionError:
if len(hidden_activation_functions) > len(hidden_sizes):
print(
"Too many hidden activation functions, must have length(hidden sizes) "
)
else:
print(
"Too few hidden activation functions, must have length(hidden sizes) "
)
exit(1)
except TypeError:
print(
"Either hidden_activation_functions or hidden_sizes is None; Make both None or neither."
)
self.model = Sequential()
self.model.add(InputLayer(input_shape=(state_size, )))
if hidden_activation_functions is not None and hidden_sizes is not None:
hidden_inputs = np.roll(hidden_sizes, 1)
hidden_inputs[0] = state_size
for s, i, a in zip(hidden_sizes, hidden_inputs,
hidden_activation_functions):
self.model.add(Dense(s, input_dim=i, activation=a))
self.model.add(Dropout(0.1))
self.model.add(
Dense(action_size,
input_dim=state_size,
activation=output_activation_function))
self.model.compile(loss='mse', optimizer='adam', metrics=['mse'])
class Discriminator(Model):
def __init__(self, num_layer: int, num_cat: int):
super().__init__()
self.num_layer = num_layer
self.num_cat = num_cat
def build(self, input_shape):
kernel_size = 5
stride_size = 2
width = input_shape[2]
height = input_shape[1]
num_channel = input_shape[3]
next_channel = num_channel * 32
kernel_init = keras.initializers.RandomNormal(stddev=0.02)
bias_init = keras.initializers.zeros()
self.conv2ds = Sequential()
for i in range(self.num_layer - 1):
conv = Conv2D(next_channel,
kernel_size,
stride_size,
padding="same",
activation=tf.nn.leaky_relu,
kernel_initializer=kernel_init,
bias_initializer=bias_init,
input_shape=(height, width, num_channel))
self.conv2ds.add(conv)
width = conv.output_shape[2]
height = conv.output_shape[1]
num_channel = next_channel
next_channel *= stride_size
conv = Conv2D(1 + self.num_cat, (height, width), (height, width),
padding="valid",
kernel_initializer=kernel_init,
bias_initializer=bias_init)
self.conv2ds.add(conv)
self.flatten = Flatten()
def call(self, inputs: keras.layers.Input, **kwargs):
output = inputs
output = self.conv2ds(output)
output = self.flatten(output)
classify_result: tf.Tensor = output[:, :1]
cat_result: tf.Tensor = output[:, 1:]
return classify_result, cat_result
def __init__(
self,
dim: int,
layer_dims: List[Union[float, int]] = [0.5, 0.5, 1, 1, 2, 2, 4],
):
super().__init__()
dims = [int(dim * f) for f in layer_dims]
layers = [self._make_layers(dim, i) for i, dim in enumerate(dims)]
layers += [ConvBlock(dim * 4, 4, 2, False)]
self.layer = Sequential(layers)
self.head = Sequential([Flatten(), Dense(1)])
def __init__(self, session=None, dir_loc=None):
# Initilize session and tensorboard dirs
self.session = tf.Session() if session is None else session
self.dir_loc = './logs' if dir_loc is None else dir_loc
self._writer = None
# Initialize feature extractor
self.extractor = Sequential()
# Set idication for when model is build
self.built = False
def __init__(self,
learning_rate: float = 0.0001,
hidden_layers_count: int = 0,
neurons_per_hidden_layer: int = 0):
self.model = Sequential()
for i in range(hidden_layers_count):
self.model.add(Dense(neurons_per_hidden_layer, activation=tanh))
self.model.add(Dense(1, activation=softmax, use_bias=True))
self.model.compile(loss=mse, optimizer=Adam(lr=learning_rate))
def __init__(self, game: GridGame):
super().__init__(game)
# game params
self.board_height = game.board_height
self.board_width = game.board_width
example_board = game.create_board()
self.action_size = len(game.get_valid_moves(example_board))
self.epochs_completed = 0
self.epochs_to_train = 100
args = Namespace(lr=0.001,
dropout=0.3,
epochs=10,
batch_size=64,
num_channels=512)
self.checkpoint_name = 'random weights'
self.args = args
num_channels = 512
kernel_size = [3, 3]
dropout = 0.3
model = Sequential()
# regularizer = regularizers.l2(0.00006)
regularizer = regularizers.l2(0.0001)
model.add(Conv2D(num_channels,
kernel_size,
padding='same',
activation='relu',
input_shape=(self.board_height, self.board_width, 1),
activity_regularizer=regularizer))
model.add(Conv2D(num_channels,
kernel_size,
padding='same',
activation='relu',
activity_regularizer=regularizer))
model.add(Conv2D(num_channels,
kernel_size,
activation='relu',
activity_regularizer=regularizer))
model.add(Conv2D(num_channels,
kernel_size,
activation='relu',
activity_regularizer=regularizer))
model.add(Dropout(dropout))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(self.action_size + 1))
model.compile('adam', 'mean_squared_error')
self.model = model
