def define_model(self):
input_img = Input(shape=(self.model_parameters.img_height,
self.model_parameters.img_width,
self.model_parameters.num_channels))
x = layers.Conv2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')(input_img)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU()(x)
model = Model(name=self.model_name, inputs=input_img, outputs=x)
return model
def get_model(self, numpy_matrix_embeddings: numpy.ndarray, **kwargs) -> Model:
# get default parameters
dropout = kwargs.get('dropout', 0.9)
assert isinstance(dropout, float)
lstm_layer_size = kwargs.get('lstm_layer_size', 64)
assert isinstance(lstm_layer_size, int)
# this is the placeholder tensor for the input sequences
# input sequence is a numpy array of word indices
input_sequence = Input(shape=(None,), dtype='int32', name='input_sequence')
# this embedding layer will transform the sequences of integers
# into vectors of size dimension of embeddings
embedded = Embedding(numpy_matrix_embeddings.shape[0], numpy_matrix_embeddings.shape[1],
weights=[numpy_matrix_embeddings], mask_zero=True)(input_sequence)
# bidirectional LSTM using the neat Keras wrapper
lstm_output1 = Bidirectional(LSTM(lstm_layer_size, return_sequences=True))(embedded)
# stack the next one
lstm_output2 = Bidirectional(LSTM(lstm_layer_size))(lstm_output1)
# dropout
dropout_layer = Dropout(dropout)(lstm_output2)
# one extra dense layer
fully_connected = Dense(lstm_layer_size / 2, activation='relu')(dropout_layer)
# classification
output = Dense(2, activation='softmax')(fully_connected)
model = Model(inputs=[input_sequence], outputs=output)
print("Compiling model")
model.compile('adam', loss=categorical_crossentropy)
return model
def create_preprocessing_model(
output_shape=(224, 224, 3), model_variant="senet50"):
"""Preprocessing model. Use this as the first model to preprocess images before using the original models.
Alternatively, preprocessing can be done using numpy/ PIL and on Android, Android.graphics.bitmap.createBitmap, but
they're are not consistent.
The values used as arguments to [DepthwiseNormalization] were taken from [keras_vggface.utils.preprocess_input],
specifically, the version 2 parameters. This means this function/ preprocessing model only supports
RESNET50 and SENET50, since version 1 is used for VGG16.
Args:
model_variant: either vgg16, resnet50 or senet50.
output_shape: The output shape for the processing model, which should match the input
shape of the subsequent model, formatted with channels-last
Returns:
model: Keras model containing layers to preprocess images as per the original keras-vggface/utils.preprocess_inputs
"""
input_shape = (None, None, 3)
input = Input(shape=input_shape, batch_size=1, name="input_image")
x = ChannelReversal()(input)
x = Resizing(output_shape[0],
output_shape[1],
interpolation='bilinear',
name="Resize")(x)
if model_variant == "senet50" or model_variant == "resnet50":
output = DepthwiseNormalization([91.4953, 103.8827, 131.0912])(x)
elif model_variant == "vgg16":
output = DepthwiseNormalization([93.5940, 104.7624, 129.1863])(x)
else:
raise ValueError(f"Unsupported model_variant: {model_variant}")
model = Model(input, output, name='preprocessing')
return model
def darknet4det4(input_shape, output_channels):
inputs = Input(shape=(input_shape[0], input_shape[1], 3))
darknet = Model(inputs, darknet_body(inputs))
# downsample
subsample_x = compose(
DarknetConv2D_BN_Leaky(1024, (3, 3), strides=(1, 1)),
DarknetConv2D_BN_Leaky(512, (3, 3), strides=(1, 1)),
DarknetConv2D_BN_Leaky(512, (3, 3), strides=(2, 2), padding='SAME')
)(darknet.output)
_, y4 = make_last_layers(subsample_x, 512, output_channels, output_layer_name="output4")
x, y1 = make_last_layers(darknet.output, 512, output_channels, output_layer_name='output1')
# print(type(x))
# upsmaple
x = compose(
DarknetConv2D_BN_Leaky(256, (1, 1)),
UpSampling2D(2))(x)
x = Concatenate()([x, darknet.layers[152].output])
x, y2 = make_last_layers(x, 256, output_channels, output_layer_name='output2')
# upsample
x = compose(
DarknetConv2D_BN_Leaky(128, (1, 1)),
UpSampling2D(2))(x)
x = Concatenate()([x, darknet.layers[92].output])
x, y3 = make_last_layers(x, 128, output_channels, output_layer_name='output3')
# order in descending feature size
return Model(inputs, [y3, y2, y1, y4])
def ResneXt_IN(input_shape=None,
cardinality=8,
weight_decay=5e-4,
classes=None):
# TODO 主函数
# model = ResneXt_IN((1, 11, 11, 200), cardinality=8, classes=16)
# 判断底层，tf，channel在最后
_handle_dim_ordering()
if len(input_shape) != 4:
raise Exception(
"Input shape should be a tuple (nb_channels, kernel_dim1, kernel_dim2, kernel_dim3)"
)
print('original input shape:', input_shape)
# orignal input shape（1，11，11，200）
if K.image_data_format() == 'channels_last':
input_shape = (input_shape[1], input_shape[2], input_shape[3],
input_shape[0])
print('change input shape:', input_shape)
# TODO 数据输入
input = Input(shape=input_shape)
# TODO 网络搭建
x_dense = __create_res_next(classes, input, cardinality, weight_decay)
model = Model(input, x_dense, name='resnext_IN')
return model
def make_deep_autoencoder(dims,
act='relu',
init='glorot_uniform',
layer_decorator: typing.Optional[typing.Callable[
[Layer, bool], Layer]] = None,
compile=True) -> typing.Tuple[Model, Model]:
r"""Build a fully-connected symmetric autoencoder model.
:param dims: Per-layer neuron counts for the entire AE.
:param act: Activation function for all neurons (except input and output layers)
:param init: Initialization for the weights of every neuron.
:param layer_decorator: Callable that can modify every layer of the network.
:param compile: Compile the model now or leave it up to the caller.
:return: (ae_model, encoder_model), Model of autoencoder and model of encoder
"""
input_img = Input(shape=(dims[0], ), name='input')
n_stacks = len(dims) - 1
assert n_stacks > 0
x = input_img
# internal layers in encoder
for i in range(n_stacks - 1):
x = Dense(dims[i + 1],
activation=act,
kernel_initializer=init,
name='encoder_%d' % i)(x)
if layer_decorator:
x = layer_decorator(x, is_output=False)
# encoder output layer (the last hidden encoding layer in the autoencoder)
# features are extracted from here
x = Dense(dims[-1],
kernel_initializer=init,
name='encoder_%d' % (n_stacks - 1))(x)
if layer_decorator:
x = layer_decorator(x, is_output=False)
encoded = x
# internal layers in decoder
for i in range(n_stacks - 1, 0, -1):
x = Dense(dims[i],
activation=act,
kernel_initializer=init,
name='decoder_%d' % i)(x)
if layer_decorator:
x = layer_decorator(x, is_output=False)
# output
x = Dense(dims[0], kernel_initializer=init, name='decoder_0')(x)
if layer_decorator:
x = layer_decorator(x, is_output=True)
decoded = x
encoder = Model(inputs=input_img, outputs=encoded, name='encoder')
autoencoder = Model(inputs=input_img, outputs=decoded, name='AE')
if compile:
autoencoder.compile(optimizer='adadelta', loss='mse')
return autoencoder, encoder
def build_train_model(self):
shared_model = self.base_network_build_fn()
# shared_model = build_multi_attention_model(self.input_steps)
# shared_model = build_stacked_rnn_model(self)
t_status = shared_model.output
p = layers.Dense(self.action_num, activation='softmax',
name='p')(t_status)
r = Input(shape=(self.action_num, ), name='r')
q = Input(shape=(self.action_num, ), name='q')
loss = PPO_Loss()([r, q, p])
train_input = shared_model.input + [r, q]
model = Model(train_input, loss, name=self.model_type)
# 减小学习率，避免策略改变的太快
model.compile(optimizer=Adam(lr=self.lr), loss=None)
return model
def _build_net(self):
inputs = Input(shape=(self.n_features,))
x = Dense(32, activation='relu', kernel_regularizer=l2(self.l2))(inputs)
x = Dense(16, activation='relu', kernel_regularizer=l2(self.l2))(x)
output = Dense(self.n_actions, activation='softmax', kernel_regularizer=l2(self.l2))(x)
self.model = Model(inputs=inputs, outputs=output)
self.model.compile(optimizer=Adam(learning_rate=self.lr), loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
def policy_model(input_dim=(6, 6, 1), depth=5) -> Model:
inp = Input(input_dim)
conv = conv_layer(inp)
res = conv
for _ in range(depth):
res = residual_layer(res)
pol = policy_layer(res)
return Model(inputs=inp, outputs=pol)
def testMultiHeadAttentionModelOutputShape(self):
max_sequence_length = 50
embedding_size = 64
inputs = Input(shape=[max_sequence_length, embedding_size])
g = attention.multihead_attention_model(inputs)
actual_output_shape = g.shape
expected_output_shape = (1, 28, 28, 1)
self.assertEqual(actual_output_shape, expected_output_shape)
def get_model(self):
input = Input(self.maxlen)
embedding = self.embedding_layer(input)
x = Bidirectional(self._RNN(128))(embedding) # LSTM or GRU
output = Dense(self.num_class, activation=self.last_activation)(x)
model = Model(inputs=input, outputs=output)
return model
def stage_5(mult=1):
x = inputs = Input([None, None, 3])
x = darknet_conv(x, 32 * mult, 3)
x = blocking_convolution(x, 64 * mult, 1)
x = blocking_convolution(x, 128 * mult, 2)
x = x_36 = blocking_convolution(x, 256 * mult, 8)
x = x_61 = blocking_convolution(x, 512 * mult, 8)
return tf.keras.Model(inputs, x)
def build_model(dim, max_seq, n_input_voca, n_output_voca):
#W2 = tf.keras.layers.Dense(n_output_voca, use_bias=False)
#W2 = K.random_normal_variable(shape=(n_output_voca, dim), mean=0, scale=1)
np_val = np.reshape(np.random.normal(size=n_output_voca * dim),
[n_output_voca, dim])
W2 = K.constant(np_val)
code_id = Input(shape=[1], name=str_code_id)
token_ids = Input(shape=(max_seq, ), dtype=tf.int32, name=str_desc_tokens)
W1 = tf.keras.layers.Embedding(n_input_voca, dim)
h0 = W1(code_id)
#logits = W2(h)
#logits = MyLayer(n_output_voca, dim, max_seq)(h)
h = tf.reshape(h0, [-1, dim])
h = tf.nn.l2_normalize(h, -1)
W2 = tf.nn.l2_normalize(W2, -1)
logits = tf.matmul(h, W2, transpose_b=True)
#logits = tf.reshape(logits, [-1, max_seq, n_output_voca])
log_probs = tf.nn.log_softmax(logits, axis=-1)
y = tf.one_hot(token_ids,
depth=n_output_voca) #[batch, max_seq, n_output_voca]
pos_val = logits * y # [ batch, max_seq, voca]
neg_val = logits - tf.reduce_sum(pos_val, axis=1) #[ batch, voca]
t = tf.reduce_sum(pos_val, axis=2) # [batch, max_seq]
correct_map = tf.expand_dims(t, 2) # [batch, max_seq, 1]
print(correct_map.shape)
#logits_correct = tf.expand_dims(tf.reduce_sum(logits * y, axis=-1), -1)
wrong_map = tf.expand_dims(neg_val, 1) # [batch, 1, voca]
print(wrong_map.shape)
t = wrong_map - correct_map + 1
print(t.shape)
loss = tf.reduce_mean(tf.math.maximum(t, 0), axis=-1)
mask = tf.cast(tf.not_equal(token_ids, 0), tf.float32) # batch, seq_len
print(mask.shape)
# loss = -tf.reduce_sum(input_tensor=log_probs * y, axis=[-1])
loss = mask * loss
loss = tf.reduce_sum(loss, axis=1) # over the sequence
loss = tf.reduce_mean(loss)
print(loss.shape)
model = Model(inputs=[code_id, token_ids], outputs=h0)
return loss, model
def generate_categorical_input(self, column, column_config: Dict):
hash_bucket_size, embedding_dimensions, feature_column = \
self.create_categorical_column_with_hash_bucket(column, column_config)
embedding_feature_column = \
tf.feature_column.embedding_column(
feature_column, dimension=embedding_dimensions)
keras_input = Input(name=column.name, shape=[embedding_dimensions])
return embedding_feature_column, keras_input, keras_input
def value_model(input_dim=(6, 6, 1), depth=5) -> Model:
inp = Input(input_dim)
conv = conv_layer(inp)
res = conv
for _ in range(depth):
res = residual_layer(res)
val = value_layer(res)
return Model(inputs=inp, outputs=val)
def runRNN():
# Assumes you're in the root level of the dataset directory.
# If you aren't, you'll need to change the relative paths here.
train_data = prepareData('./train')
test_data = prepareData('./test')
for text_batch, label_batch in train_data.take(1):
print(text_batch.numpy()[0])
print(label_batch.numpy()[0]) # 0 = negative, 1 = positive
model = Sequential()
# ----- 1. INPUT
# We need this to use the TextVectorization layer next.
model.add(Input(shape=(1,), dtype="string"))
# ----- 2. TEXT VECTORIZATION
# This layer processes the input string and turns it into a sequence of
# max_len integers, each of which maps to a certain token.
max_tokens = 1000
max_len = 100
vectorize_layer = TextVectorization(
# Max vocab size. Any words outside of the max_tokens most common ones
# will be treated the same way: as "out of vocabulary" (OOV) tokens.
max_tokens=max_tokens,
# Output integer indices, one per string token
output_mode="int",
# Always pad or truncate to exactly this many tokens
output_sequence_length=max_len,
)
# Call adapt(), which fits the TextVectorization layer to our text dataset.
# This is when the max_tokens most common words (i.e. the vocabulary) are selected.
train_texts = train_data.map(lambda text, label: text)
vectorize_layer.adapt(train_texts)
model.add(vectorize_layer)
# ----- 3. EMBEDDING
# This layer turns each integer (representing a token) from the previous layer
# an embedding. Note that we're using max_tokens + 1 here, since there's an
# out-of-vocabulary (OOV) token that gets added to the vocab.
model.add(Embedding(max_tokens + 1, 128))
# ----- 4. RECURRENT LAYER
model.add(LSTM(64))
# ----- 5. DENSE HIDDEN LAYER
model.add(Dense(64, activation="relu"))
# ----- 6. OUTPUT
model.add(Dense(1, activation="sigmoid"))
# Compile and train the model.
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(train_data, epochs=1)
model.save_weights('rnn')
def define_model(self):
input_img = Input(shape=[
self.model_parameters.img_height,
self.model_parameters.img_width,
self.model_parameters.num_channels,
])
class_id = Input(shape=[1])
embedded_id = layers.Embedding(input_dim=10, output_dim=50)(class_id)
embedded_id = layers.Dense(units=input_img.shape[1] *
input_img.shape[2])(embedded_id)
embedded_id = layers.Flatten()(embedded_id)
x = layers.Conv2D(filters=128,
kernel_size=(3, 3),
strides=(1, 1),
padding='same')(input_img)
x = layers.BatchNormalization(momentum=0.9)(x)
x = layers.LeakyReLU(alpha=0.1)(x)
x = layers.Conv2D(filters=128,
kernel_size=(4, 4),
strides=(2, 2),
padding='same')(x)
x = layers.BatchNormalization(momentum=0.9)(x)
x = layers.LeakyReLU(alpha=0.1)(x)
x = layers.Conv2D(filters=128,
kernel_size=(4, 4),
strides=(2, 2),
padding='same')(x)
x = layers.BatchNormalization(momentum=0.9)(x)
x = layers.LeakyReLU(alpha=0.1)(x)
x = layers.Flatten()(x)
x = layers.Concatenate()([x, embedded_id])
x = layers.Dense(units=512, activation='relu')(x)
x = layers.Dense(units=1)(x)
model = Model(name=self.model_name,
inputs=[input_img, class_id],
outputs=x)
return model
def test_invalid_variable_input(self):
with context.eager_mode():
inputs = Input(shape=(1,))
outputs = testing_utils.Bias()(inputs)
model = Model(inputs, outputs)
with self.assertRaisesRegexp(
ValueError,
'Expected a symbolic Tensors or a callable for the loss value'):
model.add_loss(model.weights[0])
def test_invalid_constant_input(self):
inputs = Input(shape=(1, ))
outputs = testing_utils.Bias()(inputs)
model = Model(inputs, outputs)
with self.assertRaisesRegex(
ValueError,
'Expected a symbolic Tensors or a callable for the loss value'
):
model.add_loss(1.)
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)
def __init__(self, game, encoder):
"""
NNet model, copied from Othello NNet, with reduced fully connected layers fc1 and fc2 and reduced nnet_args.num_channels
:param game: game configuration
:param encoder: Encoder, used to encode game boards
"""
from rts.src.config_class import CONFIG
# game params
self.board_x, self.board_y, num_encoders = game.getBoardSize()
self.action_size = game.getActionSize()
"""
num_encoders = CONFIG.nnet_args.encoder.num_encoders
"""
num_encoders = encoder.num_encoders
# Neural Net
self.input_boards = Input(shape=(
self.board_x, self.board_y,
num_encoders)) # s: batch_size x board_x x board_y x num_encoders
x_image = Reshape(
(self.board_x, self.board_y, num_encoders)
)(self.input_boards) # batch_size x board_x x board_y x num_encoders
h_conv1 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(
CONFIG.nnet_args.num_channels, 3, padding='same', use_bias=False)(
x_image))) # batch_size x board_x x board_y x num_channels
h_conv2 = Activation('relu')(BatchNormalization(axis=3)(Conv2D(
CONFIG.nnet_args.num_channels, 3, padding='same', use_bias=False)(
h_conv1))) # batch_size x board_x x board_y x num_channels
h_conv3 = Activation('relu')(
BatchNormalization(axis=3)(Conv2D(CONFIG.nnet_args.num_channels,
3,
padding='valid',
use_bias=False)(h_conv2))
) # batch_size x (board_x-2) x (board_y-2) x num_channels
h_conv4 = Activation('relu')(
BatchNormalization(axis=3)(Conv2D(CONFIG.nnet_args.num_channels,
3,
padding='valid',
use_bias=False)(h_conv3))
) # batch_size x (board_x-4) x (board_y-4) x num_channels
h_conv4_flat = Flatten()(h_conv4)
s_fc1 = Dropout(CONFIG.nnet_args.dropout)(Activation('relu')(
BatchNormalization(axis=1)(Dense(
256, use_bias=False)(h_conv4_flat)))) # batch_size x 1024
s_fc2 = Dropout(CONFIG.nnet_args.dropout)(Activation('relu')(
BatchNormalization(axis=1)(Dense(
128, use_bias=False)(s_fc1)))) # batch_size x 1024
self.pi = Dense(self.action_size, activation='softmax',
name='pi')(s_fc2) # batch_size x self.action_size
self.v = Dense(1, activation='tanh', name='v')(s_fc2) # batch_size x 1
self.model = Model(inputs=self.input_boards, outputs=[self.pi, self.v])
self.model.compile(
loss=['categorical_crossentropy', 'mean_squared_error'],
optimizer=Adam(CONFIG.nnet_args.lr))
def create_deep_autoencoder():
input_shape = Input(shape=(INPUT_LENGTH, ))
model = Model(input_shape, create_deep_decoder(create_deep_encoder(input_shape)))
model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy')
print(model.summary())
return model
def Darknet(name=None):
x = inputs = Input([None, None, 3])
x = DarknetConv(x, 32, 3)
x = DarknetBlock(x, 64, 1)
x = DarknetBlock(x, 128, 2) # skip connection
x = x_36 = DarknetBlock(x, 256, 8) # skip connection
x = x_61 = DarknetBlock(x, 512, 8)
x = DarknetBlock(x, 1024, 4)
return tf.keras.Model(inputs, (x_36, x_61, x), name=name)
def Build(self):
# input layer is from GlobalAveragePooling:
inLayer = Input([self.latentSize])
# reexpand the input from flat:
net = Dense(self.intermediateSize, activation='relu')(inLayer)
net = Dense(np.prod(self.inputShape), activation='sigmoid')(net)
net = Reshape(self.inputShape)(net)
return Model(inLayer, net)
def get_model(self):
input = Input(self.maxlen)
embedding = self.embedding_layer(input)
x = GlobalAveragePooling1D()(embedding)
output = Dense(self.num_class, activation=self.last_activation)(x)
model = Model(inputs=input, outputs=output)
return model
def build_model(fragment_length, nb_filters, nb_output_bins, dilation_depth, nb_stacks, use_skip_connections,
learn_all_outputs, _log, desired_sample_rate, use_bias, res_l2, final_l2):
def residual_block(x):
original_x = x
# TODO: initalization, regularization?
# Note: The AtrousConvolution1D with the 'causal' flag is implemented in github.com/basveeling/keras#@wavenet.
tanh_out = CausalAtrousConvolution1D(nb_filters, 2, dilation_rate=2 ** i, padding='valid', causal=True,
use_bias=use_bias,
name='dilated_conv_%d_tanh_s%d' % (2 ** i, s), activation='tanh',
kernel_regularizer=l2(res_l2))(x)
sigm_out = CausalAtrousConvolution1D(nb_filters, 2, dilation_rate=2 ** i, padding='valid', causal=True,
use_bias=use_bias,
name='dilated_conv_%d_sigm_s%d' % (2 ** i, s), activation='sigmoid',
kernel_regularizer=l2(res_l2))(x)
x = layers.Multiply(name='gated_activation_%d_s%d' % (i, s))([tanh_out, sigm_out])
res_x = layers.Convolution1D(nb_filters, 1, padding='same', use_bias=use_bias,
kernel_regularizer=l2(res_l2))(x)
skip_x = layers.Convolution1D(nb_filters, 1, padding='same', use_bias=use_bias,
kernel_regularizer=l2(res_l2))(x)
res_x = layers.add([original_x, res_x])
return res_x, skip_x
input = Input(shape=(fragment_length, nb_output_bins), name='input_part')
out = input
skip_connections = []
out = CausalAtrousConvolution1D(nb_filters, 2,
dilation_rate=1,
padding='valid',
causal=True,
name='initial_causal_conv'
)(out)
for s in range(nb_stacks):
for i in range(0, dilation_depth + 1):
out, skip_out = residual_block(out)
skip_connections.append(skip_out)
if use_skip_connections:
out = layers.add(skip_connections)
out = layers.Activation('relu')(out)
out = layers.Convolution1D(nb_output_bins, 1, padding='same',
kernel_regularizer=l2(final_l2))(out)
out = layers.Activation('relu')(out)
out = layers.Convolution1D(nb_output_bins, 1, padding='same')(out)
if not learn_all_outputs:
raise DeprecationWarning('Learning on just all outputs is wasteful, now learning only inside receptive field.')
out = layers.Lambda(lambda x: x[:, -1, :], output_shape=(out._keras_shape[-1],))(
out) # Based on gif in deepmind blog: take last output?
out = layers.Activation('softmax', name="output_softmax")(out)
model = Model(input, out)
receptive_field, receptive_field_ms = compute_receptive_field()
_log.info('Receptive Field: %d (%dms)' % (receptive_field, int(receptive_field_ms)))
return model
def combine_streams(models: List[StreamModel], combine_fn=None):
outs = []
ins = []
for stream in models:
inp = Input(stream.get_input_shape())
ins.append(inp)
outs.append(stream.get_stream_model(inp))
out = combine_fn(ins)
return Model(ins, out)
def backbone(name=None, size=(None, None)):
x = inputs = Input([size[0], size[0], 3])
x = darknet_conv(x, 32, 3)
x = blocking_convolution(x, 64, 1)
x = blocking_convolution(x, 128, 2) # skip connection
x = x_36 = blocking_convolution(x, 256, 8) # skip connection
x = x_61 = blocking_convolution(x, 512, 8)
x = blocking_convolution(x, 1024, 4)
return tf.keras.Model(inputs, (x_36, x_61, x), name=name)
def Build(self):
sequence_input = Input((self.encoder_frames_no, 128, 128, 3), self.batch_size,
name="nvae_seq_input")
mask_input = Input((128, 128, 1), self.batch_size,
name="nvae_mask_input")
encoder_output = self.encoder_model(sequence_input)
self.mean_1024 = encoder_output[0]
self.stddev_1024 = encoder_output[1]
self.skip_4x4x512 = encoder_output[2]
self.skip_8x8x256 = encoder_output[3]
self.skip_16x16x128 = encoder_output[4]
self.skip_32x32x64 = encoder_output[5]
self.skip_64x64x32 = encoder_output[6]
self.skip_128x128x16 = encoder_output[7]
mask_encoder_output = self.mask_encoder_model(mask_input)
self.mask_1024 = mask_encoder_output[0]
self.mask4x4x512 = mask_encoder_output[1]
self.mask8x8x256 = mask_encoder_output[2]
self.mask16x16x128 = mask_encoder_output[3]
self.mask32x32x64 = mask_encoder_output[4]
self.mask64x64x32 = mask_encoder_output[5]
self.mask128x128x16 = mask_encoder_output[6]
decoder_output = self.decoder_model(encoder_output + mask_encoder_output)
net = decoder_output[0]
self.mean_1024 = decoder_output[1]
self.stddev_1024 = decoder_output[2]
self.mean_4x4x512 = decoder_output[3]
self.stddev_4x4x512 = decoder_output[4]
self.mean_8x8x256 = decoder_output[5]
self.stddev_8x8x256 = decoder_output[6]
self.mean_16x16x128 = decoder_output[7]
self.stddev_16x16x128 = decoder_output[8]
self.mean_32x32x64 = decoder_output[9]
self.stddev_32x32x64 = decoder_output[10]
self.mean_64x64x32 = decoder_output[11]
self.stddev_64x64x32 = decoder_output[12]
self.mean_128x128x16 = decoder_output[13]
self.stddev_128x128x16 = decoder_output[14]
net = DummyMaskLayer()(net)
return Model(inputs=[sequence_input, mask_input], outputs=net)
def build_model(num_lags,
model="MLP",
summary=True,
date_time=False,
combined_model=False,
event_info=False):
assert model in models
input_lags = Input(shape=(num_lags, ), name="pickup_lags")
inputs = []
concat = []
x = input_lags
if event_info:
input_events = Input(shape=(max_count, ), name="events")
events_feat = Embedding(max_count, 8)(input_events)
events_feat = LSTM(16)(events_feat)
inputs.append(input_events)
concat.append(events_feat)
if date_time:
input_date_time = Input(shape=(75, ), name="date_time")
inputs.append(input_date_time)
concat.append(input_date_time)
if combined_model:
pickup_zone = Input(shape=(4, ), name="pickup_zone")
inputs.append(pickup_zone)
concat.append(pickup_zone)
if model.upper() == "MLP":
# x = concatenate([x] + concat)
x = MLP(x)
elif model.upper() == "LSTM":
x = Reshape((1, 48))(x)
x = SLSTM(x)
if len(inputs) > 0:
x = concatenate([x] + concat)
# x = Dense(units=10, activation="relu")(x)
inputs.append(input_lags)
else:
inputs = input_lags
preds = Dense(units=1, name="predicted")(x)
model = Model(inputs, preds)
model.compile(loss="mean_squared_error", optimizer="adam")
if summary:
print(model.summary())
return model