def box_convolution(input, box_filters, l2):
reduce = Conv3D(box_filters, 1, kernel_regularizer=l2)(input)
squash = Conv3D(box_filters, (FOUR, FOUR, FOUR), kernel_regularizer=l2)(reduce)
gather = Reshape((box_filters,))(squash)
repeat = RepeatVector(FOUR * FOUR * FOUR)(gather)
spread = Reshape((FOUR, FOUR, FOUR, box_filters))(repeat)
return spread
def demo_create_encoder(latent_dim, cat_dim, window_size, input_dim):
input_layer = Input(shape=(window_size, input_dim))
code = TimeDistributed(Dense(64, activation='linear'))(input_layer)
code = Bidirectional(LSTM(128, return_sequences=True))(code)
code = BatchNormalization()(code)
code = ELU()(code)
code = Bidirectional(LSTM(64))(code)
code = BatchNormalization()(code)
code = ELU()(code)
cat = Dense(64)(code)
cat = BatchNormalization()(cat)
cat = PReLU()(cat)
cat = Dense(cat_dim, activation='softmax')(cat)
latent_repr = Dense(64)(code)
latent_repr = BatchNormalization()(latent_repr)
latent_repr = PReLU()(latent_repr)
latent_repr = Dense(latent_dim, activation='linear')(latent_repr)
decode = Concatenate()([latent_repr, cat])
decode = RepeatVector(window_size)(decode)
decode = Bidirectional(LSTM(64, return_sequences=True))(decode)
decode = ELU()(decode)
decode = Bidirectional(LSTM(128, return_sequences=True))(decode)
decode = ELU()(decode)
decode = TimeDistributed(Dense(64))(decode)
decode = ELU()(decode)
decode = TimeDistributed(Dense(input_dim, activation='linear'))(decode)
error = Subtract()([input_layer, decode])
return Model(input_layer, [decode, latent_repr, cat, error])
def create_model(steps_before, steps_after, feature_count):
"""
creates, compiles and returns a RNN model
@param steps_before: the number of previous time steps (input)
@param steps_after: the number of posterior time steps (output or predictions)
@param feature_count: the number of features in the model
@param hidden_neurons: the number of hidden neurons per LSTM layer
"""
DROPOUT = 0.5
LAYERS = 2
hidden_neurons = 300
model = Sequential()
model.add(
LSTM(input_dim=feature_count,
output_dim=hidden_neurons,
return_sequences=False))
model.add(RepeatVector(steps_after))
model.add(LSTM(output_dim=hidden_neurons, return_sequences=True))
model.add(TimeDistributed(Dense(feature_count)))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error',
optimizer='rmsprop',
metrics=['accuracy'])
return model
def __init__(self):
#self.inception = InceptionResNetV2(weights=None, include_top=True)
#self.inception.load_weights('/data/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5')
#inception.graph = tf.get_default_graph()
embed_input = Input(shape=(1000,))
encoder_input = Input(shape=(256, 256, 1,))
#encoder_output=GaussianNoise(0.1)(encoder_input)
encoder_output = Conv2D(64, (3, 3), activation='relu', padding='same', strides=2)(encoder_input)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(128, (3, 3), activation='relu', padding='same', strides=2)(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same', strides=2)(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu', padding='same')(encoder_output) # Fusion
fusion_output = RepeatVector(32 * 32)(embed_input)
fusion_output = Reshape(([32, 32, 1000]))(fusion_output)
fusion_output = concatenate([encoder_output, fusion_output], axis=3)
fusion_output = Conv2D(256, (1, 1), activation='relu', padding='same')(fusion_output) # Decoder
decoder_output = Conv2D(128, (3, 3), activation='relu', padding='same')(fusion_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(64, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(32, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(16, (3, 3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(2, (3, 3), activation='tanh', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
model = Model(inputs=[encoder_input, embed_input], outputs=decoder_output)
model.compile(optimizer="adagrad", loss='mse')
self.model = model
def build_model():
# epoch, dropout = best_model()
epoch, dropout = 5, 0.2
print('EPOCH = ', epoch)
print('DROPOUT = ', dropout)
model = Sequential()
model.add(
Embedding(input_dim=ger_vocab_size,
output_dim=128,
input_length=11))
model.add(LSTM(128))
model.add(RepeatVector(11))
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(dropout))
model.add(Dense(eng_vocab_size, activation='softmax'))
model.compile(optimizer=RMSprop(lr=0.01),
loss='sparse_categorical_crossentropy',
metrics=['acc'])
model.summary()
# Train model
history = model.fit(X_train,
y_train.reshape(y_train.shape[0],
y_train.shape[1], 1),
epochs=epoch,
batch_size=128,
verbose=1,
validation_split=0.2)
# Evaluate the model
loss, accuracy = model.evaluate(X_test,
y_test.reshape(
y_test.shape[0],
y_test.shape[1], 1),
verbose=1)
print('Accuracy: %f' % (accuracy * 100))
def display():
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
display()
def __init__(self, input_shape, output_size):
image_model = tf.keras.Sequential()
image_model.add(
Conv2D(32, (3, 3),
padding='valid',
activation='relu',
input_shape=input_shape))
image_model.add(Conv2D(32, (3, 3), padding='valid', activation='relu'))
image_model.add(MaxPooling2D(pool_size=(2, 2)))
image_model.add(Dropout(0.25))
image_model.add(Conv2D(64, (3, 3), padding='valid', activation='relu'))
image_model.add(Conv2D(64, (3, 3), padding='valid', activation='relu'))
image_model.add(MaxPooling2D(pool_size=(2, 2)))
image_model.add(Dropout(0.25))
image_model.add(Conv2D(128, (3, 3), padding='valid',
activation='relu'))
image_model.add(Conv2D(128, (3, 3), padding='valid',
activation='relu'))
image_model.add(MaxPooling2D(pool_size=(2, 2)))
image_model.add(Dropout(0.25))
image_model.add(Flatten())
image_model.add(Dense(1024, activation='relu'))
image_model.add(Dropout(0.3))
image_model.add(Dense(1024, activation='relu'))
image_model.add(Dropout(0.3))
image_model.add(RepeatVector(CONTEXT_LENGTH))
visual_input = Input(shape=input_shape)
encoded_image = image_model(visual_input)
language_model = Sequential()
language_model.add(
tf.keras.layers.LSTM(128,
return_sequences=True,
input_shape=(CONTEXT_LENGTH, output_size)))
language_model.add(tf.keras.layers.LSTM(128, return_sequences=True))
textual_input = Input(shape=(CONTEXT_LENGTH, output_size))
encoded_text = language_model(textual_input)
decoder = concatenate([encoded_image, encoded_text])
decoder = tf.keras.layers.LSTM(512, return_sequences=True)(decoder)
decoder = tf.keras.layers.LSTM(512, return_sequences=False)(decoder)
decoder = Dense(output_size, activation='softmax')(decoder)
super().__init__(inputs=[visual_input, textual_input], outputs=decoder)
optimizer = RMSprop(lr=0.0001, clipvalue=1.0)
self.compile(loss='categorical_crossentropy', optimizer=optimizer)
def best_model():
epochs = [5, 10, 15, 20]
dropout_rate = [0.1, 0.2, 0.3]
list_of_all_scores = list()
list_of_scores = list()
list_of_dropout = list()
list_of_all_dropouts = list()
list_of_epochs = list()
for i in dropout_rate:
model = Sequential()
model.add(
Embedding(input_dim=ger_vocab_size,
output_dim=128,
input_length=11))
model.add(LSTM(128))
model.add(RepeatVector(11))
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(i))
model.add(Dense(eng_vocab_size, activation='softmax'))
model.compile(optimizer=RMSprop(lr=0.01),
loss='sparse_categorical_crossentropy',
metrics=['acc'])
list_of_dropout.append(i)
for e in epochs:
list_of_all_dropouts.append(i)
list_of_epochs.append(e)
model.fit(X_train,
y_train.reshape(y_train.shape[0],
y_train.shape[1], 1),
epochs=e,
batch_size=128,
verbose=1,
validation_split=0.2)
score = model.evaluate(X_test,
y_test.reshape(
y_test.shape[0],
y_test.shape[1], 1),
verbose=1)
list_of_all_scores.append(score)
if score not in list_of_scores:
list_of_scores.append(score)
#print('Dropout:', i, '\n', 'Epoch:', e, '\n', 'Score:', float(score))
lowest = min(list_of_all_scores)
num = list_of_scores.index(lowest)
epoch = list_of_epochs[num]
dropout = list_of_all_dropouts[num]
print('Lowest score:', lowest, 'Epoch:', epoch, 'Dropout', dropout)
return epoch, dropout
def __init__(self,
words,
image_count_words,
*args,
max_code_length,
activation='relu',
order_layer_output_size=1024,
kernel_shape=7,
dropout_ratio=0.25,
dense_layer_size=512,
image_out=False,
**kwargs):
super().__init__(*args, **kwargs)
self.image_out = image_out
self.voc_size = len(words)
self.image_out = image_out
self.layer_output_names = words
self.image_count_words = image_count_words
self.max_code_length = max_code_length
self.shallow_cnn_unit = ShallowCnnUnit(
image_count_words=image_count_words,
kernel_shape=kernel_shape,
dropout_ratio=dropout_ratio,
activation=activation,
name='cnn_unit')
self.parallel_counter_unit = CounterUnit(layer_size=dense_layer_size,
activation=activation,
name='counter_unit')
self.ordering_layers = [
Flatten(name='ordering_flatten'),
Dense(1024, activation=activation, name='ordering_1'),
Dropout(dropout_ratio, name='ordering_drop_1'),
Dense(1024, activation=activation, name='ordering_2'),
Dropout(dropout_ratio, name='ordering_drop_2'),
Dense(order_layer_output_size,
activation=activation,
name='ordering_3')
]
self.repeat_image_layer = RepeatVector(max_code_length)
self.language_model_layers = [
tf.keras.layers.LSTM(128, return_sequences=True),
tf.keras.layers.LSTM(128, return_sequences=True)
]
self.decoder_layers = [
tf.keras.layers.LSTM(512, return_sequences=True),
tf.keras.layers.LSTM(512, return_sequences=True),
Dense(len(words), activation='softmax')
]
def Colorize():
embed_input = Input(shape=(1000, ))
# Encoder
encoder_input = Input(shape=(
256,
256,
1,
))
encoder_output = Conv2D(64, (3, 3),
activation='relu',
padding='same',
strides=2)(encoder_input)
encoder_output = Conv2D(128, (3, 3), activation='relu',
padding='same')(encoder_output)
encoder_output = Conv2D(128, (3, 3),
activation='relu',
padding='same',
strides=2)(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu',
padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3),
activation='relu',
padding='same',
strides=2)(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu',
padding='same')(encoder_output)
encoder_output = Conv2D(512, (3, 3), activation='relu',
padding='same')(encoder_output)
encoder_output = Conv2D(256, (3, 3), activation='relu',
padding='same')(encoder_output)
# Fusion
fusion_output = RepeatVector(32 * 32)(embed_input)
fusion_output = Reshape(([32, 32, 1000]))(fusion_output)
fusion_output = concatenate([encoder_output, fusion_output], axis=3)
fusion_output = Conv2D(256, (1, 1), activation='relu',
padding='same')(fusion_output)
# Decoder
decoder_output = Conv2D(128, (3, 3), activation='relu',
padding='same')(fusion_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(64, (3, 3), activation='relu',
padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(32, (3, 3), activation='relu',
padding='same')(decoder_output)
decoder_output = Conv2D(16, (3, 3), activation='relu',
padding='same')(decoder_output)
decoder_output = Conv2D(2, (3, 3), activation='tanh',
padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
return Model(inputs=[encoder_input, embed_input], outputs=decoder_output)
def define_model(src_vocab, tar_vocab, src_timesteps, tar_timesteps, n_units):
model = Sequential()
model.add(
Embedding(src_vocab,
n_units,
input_length=src_timesteps,
mask_zero=True))
model.add(LSTM(n_units))
model.add(RepeatVector(tar_timesteps))
model.add(LSTM(n_units, return_sequences=True))
model.add(TimeDistributed(Dense(tar_vocab, activation='softmax')))
return model
def make_lstm():
inputs = Input(shape=(timesteps, input_dim))
#lstm1, state_h, state_c = LSTM(latent_dim,return_state=True)(inputs)
encoded , state_h, state_c = LSTM(latent_dim,return_state=True)(inputs)
#encoded = LSTM(latent_dim)(inputs)
decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(input_dim, return_sequences=True)(decoded)
model1 = Model(inputs=inputs, outputs=[encoded , state_h, state_c])
sequence_autoencoder = Model(inputs, decoded)
encoder = Model(inputs, encoded, decoded,state_h,state_c)
sequence_autoencoder.compile(loss='mean_squared_error', optimizer='adam')
return sequence_autoencoder, encoder, model1
def relational_model():
image_input_shape = (img_rows, img_cols, 3)
text_inputs = Input(shape=(sequence_length, ), name='text_input')
text_x = Embedding(vocab_size, 128)(text_inputs)
text_x = LSTM(128)(text_x)
image_inputs = Input(shape=image_input_shape, name='image_input')
image_x = Lambda(process_image)(image_inputs)
image_x = Conv2D(24, kernel_size=(3, 3), strides=2,
activation='relu')(image_inputs)
image_x = BatchNormalization()(image_x)
image_x = Conv2D(24, kernel_size=(3, 3), strides=2,
activation='relu')(image_x)
image_x = BatchNormalization()(image_x)
image_x = Conv2D(24, kernel_size=(3, 3), strides=2,
activation='relu')(image_x)
image_x = BatchNormalization()(image_x)
image_x = Conv2D(24, kernel_size=(3, 3), strides=2,
activation='relu')(image_x)
image_x = BatchNormalization()(image_x)
shape = K.int_shape(image_x)
RN_inputs = Input(shape=(1, (2 * shape[3]) + K.int_shape(text_x)[1]))
RN_x = Dense(256, activation='relu')(RN_inputs)
RN_x = Dense(256, activation='relu')(RN_x)
RN_x = Dense(256, activation='relu')(RN_x)
RN_x = Dropout(.5)(RN_x)
RN_outputs = Dense(256, activation='relu')(RN_x)
RN = Model(inputs=RN_inputs, outputs=RN_outputs)
relations = Lambda(get_relation_vectors)(
image_x) # Get tensor [batch, relation_ID, relation_vectors]
question = RepeatVector(K.int_shape(relations)[1])(
text_x) # Shape question vector to same size as relations
relations = Concatenate(axis=2)([
relations, question
]) # Merge tensors [batch, relation_ID, relation_vectors, question_vector]
g = TimeDistributed(RN)(
relations) # TimeDistributed applies RN to relation vectors.
g = Lambda(lambda x: K.sum(x, axis=1))(g) # Sum over relation_ID
f = Dense(256, activation='relu')(g)
f = Dropout(.5)(f)
f = Dense(256, activation='relu')(f)
f = Dropout(.5)(f)
outputs = Dense(num_labels, activation='softmax')(f)
## Train model
model = Model(inputs=[text_inputs, image_inputs], outputs=outputs)
return model
def make_lstm():
inputs = Input(shape=(timesteps, input_dim))
encoded = LSTM(latent_dim)(inputs)
decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(input_dim, return_sequences=True)(decoded)
sequence_autoencoder = Model(inputs, decoded)
encoder = Model(inputs, encoded)
sequence_autoencoder.compile(loss='mean_squared_error', optimizer='adam')
print(sequence_autoencoder.summary())
print(encoder.summary())
return sequence_autoencoder, encoder
def attention_3d_block(inputs, SINGLE_ATTENTION_VECTOR=False):
# inputs.shape = (batch_size, time_steps, input_dim)
input_dim = int(inputs.shape[2])
TIME_STEPS = int(inputs.shape[1])
a = Permute((2, 1))(inputs)
a = Reshape(
(input_dim, TIME_STEPS)
)(a) # this line is not useful. It's just to know which dimension is what.
a = Dense(TIME_STEPS, activation='softmax')(a)
if SINGLE_ATTENTION_VECTOR:
a = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(a)
a = RepeatVector(input_dim)(a)
a_probs = Permute((2, 1), name='attention_vec')(a)
output_attention_mul = concatenate([inputs, a_probs], name='attention_mul')
return output_attention_mul
def attention_3d_block(inputs):
# inputs.shape = (batch_size, time_steps, input_dim)
input_dim = int(inputs.shape[2])
a = inputs
AveragePooling = pooling.GlobalAveragePooling1D(
data_format='channels_last')(a)
den1 = Dense(input_dim, activation='relu')(AveragePooling)
den2 = Dense(input_dim, activation='hard_sigmoid')(den1)
if SINGLE_ATTENTION_VECTOR:
a = Lambda(lambda x: K.mean(x, axis=1), name='dim_reduction')(den2)
a = RepeatVector(input_dim)(a)
a_probs = Permute((1, 2), name='attention_vec')(a)
output_attention_mul = merge.multiply([inputs, a_probs],
name='attention_mul')
# output_attention_mul = merge([inputs, a_probs], name='attention_mul', mode='mul') # 旧版本
return output_attention_mul
def __init__(self,
output_size,
*args,
context_length=CONTEXT_LENGTH,
activation='relu',
kernel_shape=3,
dropout_ratio=0.25,
**kwargs):
super().__init__(*args, **kwargs)
self.context_length = context_length
self.output_size = output_size
self.image_model_layers = [
Conv2D(32, kernel_shape, padding='valid', activation=activation),
Conv2D(32, kernel_shape, padding='valid', activation=activation),
MaxPooling2D(pool_size=(2, 2)),
Dropout(dropout_ratio),
Conv2D(64, kernel_shape, padding='valid', activation=activation),
Conv2D(64, kernel_shape, padding='valid', activation=activation),
MaxPooling2D(pool_size=(2, 2)),
Dropout(dropout_ratio),
Conv2D(128, kernel_shape, padding='valid', activation=activation),
Conv2D(128, kernel_shape, padding='valid', activation=activation),
MaxPooling2D(pool_size=(2, 2)),
Dropout(dropout_ratio),
Flatten(),
Dense(1024, activation='relu'),
Dropout(0.3),
Dense(1024, activation='relu'),
Dropout(0.3),
RepeatVector(context_length)
]
self.language_model_layers = [
tf.keras.layers.LSTM(
128, return_sequences=True
), # , input_shape=(CONTEXT_LENGTH, output_size)))
tf.keras.layers.LSTM(128, return_sequences=True)
]
self.decoder_layers = [
tf.keras.layers.LSTM(512, return_sequences=True),
tf.keras.layers.LSTM(512, return_sequences=False),
Dense(output_size, activation='softmax')
]
def build_encoder_decoder(self):
x = Input(batch_shape=(None, self.emdedding_size))
x_embed = Embedding(input_dim=self.vocab_size,
output_dim=self.emdedding_size,
weights=[self.pretrained_weights],
trainable=False)(x)
print("x_embed: ", x_embed)
h = LSTM(self.intermediate_dim,
return_sequences=False,
recurrent_dropout=0.2)(x_embed)
z_mean = Dense(self.latent_dim, name="z_mean")(h)
z_log_var = Dense(self.latent_dim, name="z_log_var")(h)
z = Sampling()([z_mean, z_log_var])
encoder = Model(x, [z_mean, z_log_var, z], name="encoder")
encoder.summary()
# build a generator that can sample sentences from the learned distribution
# we instantiate these layers separately so as to reuse them later
repeated_context = RepeatVector(self.emdedding_size)
decoder_h = LSTM(self.intermediate_dim,
return_sequences=True,
recurrent_dropout=0.2)
decoder_mean = Dense(self.emdedding_size,
activation='linear',
name='decoder_mean')
h_decoded = decoder_h(repeated_context(z))
x_decoded_mean = decoder_mean(h_decoded)
decoder_input = Input(shape=(self.latent_dim, ))
_h_decoded = decoder_h(repeated_context(decoder_input))
_x_decoded_mean = decoder_mean(_h_decoded)
_x_decoded_mean = Activation('relu', name="relu")(_x_decoded_mean)
_x_decoded_out = Reshape((4096, ))(_x_decoded_mean)
_x_decoded_out = Dense(self.emdedding_size,
activation='linear',
name='decoder_out')(_x_decoded_out)
decoder = Model(decoder_input, _x_decoded_out, name="decoder")
decoder.summary()
return encoder, decoder
def caption_model(max_len=33,
vocab_size=10431,
train=False,
feature_extractor_model=None):
print(f'Vocab size = {vocab_size}')
print(f'Max sequence lenth = {max_len}\n')
if train:
print('Creating training network. . .\nInput shape=(None,2048)')
inp_1 = Input(shape=(2048, ), name='image_embedding')
else:
print(
'Creating inference network. . .\nInput shape=(None, 299, 299, 3)')
inp_1 = feature_extractor_model.output
y = Dense(units=300, activation='relu',
name='image_embedding_dense')(inp_1)
y = RepeatVector(max_len, name='repeat_layer')(y)
inp_2 = Input(shape=(max_len, ), name='partial_captions')
x = Embedding(input_dim=vocab_size + 1,
output_dim=300,
input_length=max_len)(inp_2)
x = LSTM(units=256, return_sequences=True)(x)
x = TimeDistributed(Dense(units=300, activation='linear'))(x)
merge_layer = add([y, x], name=f'add_{train}')
z = Bidirectional(LSTM(units=256, return_sequences=False),
name='Bidirectional-LSTM1')(merge_layer)
out = Dense(units=vocab_size + 1, activation='softmax',
name='word_output')(z)
print(
f'Successfully created training network. . .\nOutput shape=(None,{vocab_size+1})\n'
)
if train:
return Model(inputs=[inp_1, inp_2], outputs=out, name='Caption-Model')
return Model(inputs=[feature_extractor_model.input, inp_2],
outputs=out,
name='c-Model')
def create_lstm_autoencoder(
input_dimension,
output_dimension=None,
units=100,
dropout_rate=0.2,
activation='relu',
optimizer='adam',
loss='mse',
stateful=False,
number_of_features=1,
) -> Model:
if output_dimension is None:
output_dimension = input_dimension
input_layer = Input(shape=(input_dimension, number_of_features))
layer = input_layer
layer = LSTM(units,
activation=activation,
stateful=stateful,
return_sequences=False)(layer)
layer = Dropout(rate=dropout_rate)(layer)
encoder = layer
layer = RepeatVector(output_dimension)(layer)
layer = Dropout(rate=dropout_rate)(layer)
layer = LSTM(units,
activation=activation,
stateful=stateful,
return_sequences=True)(layer)
layer = TimeDistributed(Dense(number_of_features))(layer)
decoder = layer
model = Model(input_layer, decoder)
model.compile(optimizer=optimizer, loss=loss)
return model
sequence_input = Input(shape=(data.shape[1], ), dtype='int32') # (Batch size,
embedded_sequences = embedding_layer(sequence_input)
x = Bidirectional(LSTM(UNITS,
return_sequences=True,
dropout=DROP,
activity_regularizer=k.regularizers.l2(REG)),
merge_mode='concat')(
embedded_sequences) # (batch_size, timesteps, units)
a = TimeDistributed(Dense(UNITS,
activity_regularizer=k.regularizers.l2(REG)))(x)
attention = TimeDistributed(Dense(1, activation='tanh', name='timeDense'))(
a) # (batch_size, timesteps, 1)
attention = Flatten()(attention) # (batch size, timesteps)
attention = Activation('softmax')(attention) # (batch, timesteps)
attention = RepeatVector(UNITS * 2)(attention) # (batch, units, timesteps)
attention = Permute([2, 1])(attention) #(batch, timesteps, units)
rejoined = multiply([x, attention])
# rejoined = k.backend.sum(rejoined, axis=-2 , keepdims=False)(rejoined)
# x = LSTM(UNITS, return_sequences=True, dropout=DROP, activity_regularizer=k.regularizers.l2(REG))(rejoined) # (batch_size, timesteps, units)
# x = TimeDistributed(Dense(UNITS, activation='relu', activity_regularizer=k.regularizers.l2(REG)))(x)
# attention = TimeDistributed(Dense(1, activation='tanh', name='timeDense'))(x) # (batch_size, timesteps, 1)
# attention = Flatten()(attention) # (batch size, timesteps)
# attention = Activation('softmax')(attention) # (batch, timesteps)
# attention = RepeatVector(UNITS)(attention) # (batch, units, timesteps)
# attention = Permute([2,1])(attention) #(batch, timesteps, units)
# rejoined = multiply([x, attention])
interm = LSTM(UNITS, activity_regularizer=k.regularizers.l2(REG),
dropout=DROP)(rejoined)
interm = Dense(UNITS,
def __init__(self, fl, mode, hparams):
"""
Initialises new DNN model based on input features_dim, labels_dim, hparams
:param features_dim: Number of input feature nodes. Integer
:param labels_dim: Number of output label nodes. Integer
:param hparams: Dict containing hyperparameter information. Dict can be created using create_hparams() function.
hparams includes: hidden_layers: List containing number of nodes in each hidden layer. [10, 20] means 10 then 20 nodes.
"""
# self.features_dim = fl.features_c_dim
# self.labels_dim = fl.labels_dim # Assuming that each task has only 1 dimensional output
self.features_dim = fl.features_c_dim + 1 # 1 for the positional argument
self.labels_dim = 1
self.numel = fl.labels.shape[1] + 1
self.hparams = hparams
self.mode = mode
self.normalise_labels = fl.normalise_labels
self.labels_scaler = fl.labels_scaler
features_in = Input(shape=(self.features_dim, ),
name='main_features_c_input')
# Selection of model
if mode == 'ann':
model = ann(self.features_dim, self.labels_dim, self.hparams)
x = model(features_in)
self.model = Model(inputs=features_in, outputs=x)
elif mode == 'ann2':
model_1 = ann(self.features_dim, 50, self.hparams)
x = model_1(features_in)
model_end = ann(50, 50, self.hparams)
end = model_end(x)
end_node = Dense(units=1,
activation='linear',
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='output_layer')(end)
model_2 = ann(50, self.labels_dim - 1, self.hparams)
x = model_2(x)
self.model = Model(inputs=features_in, outputs=[end_node, x])
elif mode == 'ann3':
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(0))(features_in)
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(1))(x)
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(2))(x)
# x = BatchNormalization()(x)
x = Dense(units=1,
activation='linear',
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_set_19')(x)
self.model = Model(inputs=features_in, outputs=x)
elif mode == 'conv1':
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='shared' + str(1))(features_in)
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(1))(x)
#x = BatchNormalization()(x)
x = Dense(units=19,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_set_19')(x)
#x = BatchNormalization()(x)
x = Reshape(target_shape=(19, 1))(x)
x = Conv1D(filters=hparams['filters'],
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
#x = BatchNormalization()(x)
x = Conv1D(filters=hparams['filters'] * 2,
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
x = Conv1D(filters=hparams['filters'] * 4,
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
#x = Permute((2,1))(x)
#x = GlobalAveragePooling1D()(x)
x = TimeDistributed(Dense(1, activation='linear'))(x)
x = Reshape(target_shape=(19, ))(x)
self.model = Model(inputs=features_in, outputs=x)
elif mode == 'conv2':
x = Dense(units=10,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Shared_e_' + str(1))(features_in)
x = Dense(units=10,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Shared_e_' + str(2))(x)
end = Dense(units=10,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Dense_e_' + str(1))(x)
end = Dense(units=10,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Dense_e_' + str(2))(end)
end_node = Dense(units=1,
activation='linear',
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='output_layer')(end)
x = Dense(units=80,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(1))(x)
x = Reshape(target_shape=(80, 1))(x)
x = Conv1D(filters=8,
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
x = MaxPooling1D(pool_size=2)(x)
x = Conv1D(filters=16,
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
x = MaxPooling1D(pool_size=2)(x)
#x = Permute((2,1))(x)
#x = GlobalAveragePooling1D()(x)
x = TimeDistributed(Dense(1, activation='linear'))(x)
x = Reshape(target_shape=(20, ))(x)
self.model = Model(inputs=features_in, outputs=[end_node, x])
elif mode == 'lstm':
x = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Shared_e_' + str(1))(features_in)
x = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Shared_e_' + str(2))(x)
end = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Dense_e_' + str(1))(x)
end = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Dense_e_' + str(2))(end)
end_node = Dense(units=1,
activation='linear',
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='output_layer')(end)
x = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(1))(x)
x = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(2))(x)
x = RepeatVector(n=20)(x)
x = LSTM(units=30, activation='relu', return_sequences=True)(x)
x = LSTM(units=30, activation='relu', return_sequences=True)(x)
x = TimeDistributed(Dense(1))(x)
x = Reshape(target_shape=(20, ))(x)
'''
x = Permute((2,1))(x)
x = GlobalAveragePooling1D()(x)
'''
self.model = Model(inputs=features_in, outputs=[end_node, x])
optimizer = Adam(clipnorm=1)
self.model.compile(optimizer=optimizer, loss='mean_squared_error')
def __init__(self, fl, mode, hparams):
"""
Initialises new DNN model based on input features_dim, labels_dim, hparams
:param features_dim: Number of input feature nodes. Integer
:param labels_dim: Number of output label nodes. Integer
:param hparams: Dict containing hyperparameter information. Dict can be created using create_hparams() function.
hparams includes: hidden_layers: List containing number of nodes in each hidden layer. [10, 20] means 10 then 20 nodes.
"""
self.features_dim = fl.features_c_dim
self.labels_dim = fl.labels_dim # Assuming that each task has only 1 dimensional output
self.hparams = hparams
self.mode = mode
self.normalise_labels = fl.normalise_labels
self.labels_scaler = fl.labels_scaler
features_in = Input(shape=(self.features_dim, ),
name='main_features_c_input')
# Selection of model
if mode == 'ann':
model = ann(self.features_dim, self.labels_dim, self.hparams)
x = model(features_in)
self.model = Model(inputs=features_in, outputs=x)
elif mode == 'ann2':
model_1 = ann(self.features_dim, 50, self.hparams)
x = model_1(features_in)
model_end = ann(50, 50, self.hparams)
end = model_end(x)
end_node = Dense(units=1,
activation='linear',
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='output_layer')(end)
model_2 = ann(50, self.labels_dim - 1, self.hparams)
x = model_2(x)
self.model = Model(inputs=features_in, outputs=[end_node, x])
elif mode == 'ann3':
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(0))(features_in)
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(1))(x)
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(2))(x)
# x = BatchNormalization()(x)
x = Dense(units=self.labels_dim,
activation='linear',
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Final')(x)
self.model = Model(inputs=features_in, outputs=x)
elif mode == 'conv1':
if fl.label_type == 'gf20':
final_dim = 20
else:
final_dim = 19
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='shared' + str(1))(features_in)
x = Dense(units=hparams['pre'],
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(1))(x)
#x = BatchNormalization()(x)
x = Dense(units=final_dim,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_set_19')(x)
#x = BatchNormalization()(x)
x = Reshape(target_shape=(final_dim, 1))(x)
x = Conv1D(filters=hparams['filters'],
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
#x = BatchNormalization()(x)
x = Conv1D(filters=hparams['filters'] * 2,
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
x = Conv1D(filters=hparams['filters'] * 4,
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
#x = Permute((2,1))(x)
#x = GlobalAveragePooling1D()(x)
x = TimeDistributed(Dense(1, activation='linear'))(x)
x = Reshape(target_shape=(final_dim, ))(x)
self.model = Model(inputs=features_in, outputs=x)
elif mode == 'conv2':
x = Dense(units=10,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Shared_e_' + str(1))(features_in)
x = Dense(units=10,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Shared_e_' + str(2))(x)
end = Dense(units=10,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Dense_e_' + str(1))(x)
end = Dense(units=10,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Dense_e_' + str(2))(end)
end_node = Dense(units=1,
activation='linear',
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='output_layer')(end)
x = Dense(units=80,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(1))(x)
x = Reshape(target_shape=(80, 1))(x)
x = Conv1D(filters=8,
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
x = MaxPooling1D(pool_size=2)(x)
x = Conv1D(filters=16,
kernel_size=3,
strides=1,
padding='same',
activation='relu')(x)
x = MaxPooling1D(pool_size=2)(x)
#x = Permute((2,1))(x)
#x = GlobalAveragePooling1D()(x)
x = TimeDistributed(Dense(1, activation='linear'))(x)
x = Reshape(target_shape=(20, ))(x)
self.model = Model(inputs=features_in, outputs=[end_node, x])
elif mode == 'lstm':
x = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Shared_e_' + str(1))(features_in)
x = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Shared_e_' + str(2))(x)
end = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Dense_e_' + str(1))(x)
end = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Dense_e_' + str(2))(end)
end_node = Dense(units=1,
activation='linear',
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='output_layer')(end)
x = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(1))(x)
x = Dense(units=20,
activation=hparams['activation'],
kernel_regularizer=regularizers.l1_l2(
l1=hparams['reg_l1'], l2=hparams['reg_l2']),
name='Pre_' + str(2))(x)
x = RepeatVector(n=20)(x)
x = LSTM(units=30, activation='relu', return_sequences=True)(x)
x = LSTM(units=30, activation='relu', return_sequences=True)(x)
x = TimeDistributed(Dense(1))(x)
x = Reshape(target_shape=(20, ))(x)
'''
x = Permute((2,1))(x)
x = GlobalAveragePooling1D()(x)
'''
self.model = Model(inputs=features_in, outputs=[end_node, x])
optimizer = Adam(learning_rate=hparams['learning_rate'], clipnorm=1)
def weighted_mse(y_true, y_pred):
loss_weights = np.sqrt(np.arange(1, 20))
#loss_weights = np.arange(1, 20)
return K.mean(K.square(y_pred - y_true) * loss_weights, axis=-1)
def haitao_error(y_true, y_pred):
diff = K.abs(
(y_true - y_pred) /
K.reshape(K.clip(K.abs(y_true[:, -1]), K.epsilon(), None),
(-1, 1)))
return 100. * K.mean(diff, axis=-1)
if hparams['loss'] == 'mape':
self.model.compile(optimizer=optimizer,
loss=MeanAbsolutePercentageError())
elif hparams['loss'] == 'haitao':
self.model.compile(optimizer=optimizer, loss=haitao_error)
elif hparams['loss'] == 'mse':
self.model.compile(optimizer=optimizer, loss='mean_squared_error')
from tensorflow.python.keras.layers import LSTM, RepeatVector, TimeDistributed, Dense
from src.helpers.helper_classifier import generate_data, invert
n_samples = 200000
n_numbers = 2
largest = 10000
alphabet = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '-', '+', ' ']
n_chars = len(alphabet)
n_in_seq_length = n_numbers * ceil(log10(largest + 1)) + n_numbers - 1
n_out_seq_length = ceil(log10(n_numbers * (largest + 1)))
n_batch = 100
n_epoch = 500
model = Sequential([
LSTM(100, input_shape=(n_in_seq_length, n_chars)),
RepeatVector(n_out_seq_length),
LSTM(50, return_sequences=True),
TimeDistributed(Dense(n_chars, activation='softmax'))
])
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
for i in range(n_epoch):
x, y = generate_data(n_samples, largest, alphabet)
model.fit(x, y, epochs=1, batch_size=n_batch)
model.save('training/keras_classifier.h5')
# evaluate on some new patterns
x, y = generate_data(n_samples, largest, alphabet)
def create_model(num_encorder_paragraph_tokens,
max_encoder_paragraph_seq_length, num_encoder_question_tokens,
max_encoder_question_seq_length, num_decoder_tokens):
hidden_units = 128 # 256, 128, 64
embed_hidden_units = 100
context_inputs = Input(shape=(None, ), name='context_inputs')
encoded_context = Embedding(input_dim=num_encorder_paragraph_tokens,
output_dim=embed_hidden_units,
input_length=max_encoder_paragraph_seq_length,
name='context_embedding')(context_inputs)
encoded_context = Dropout(0.3)(encoded_context)
question_inputs = Input(shape=(None, ), name='question_inputs')
encoded_question = Embedding(input_dim=num_encoder_question_tokens,
output_dim=embed_hidden_units,
input_length=max_encoder_question_seq_length,
name='question_embedding')(question_inputs)
encoded_question = Dropout(0.3)(encoded_question)
encoded_question = LSTM(units=embed_hidden_units,
name='question_lstm')(encoded_question)
encoded_question = RepeatVector(max_encoder_paragraph_seq_length)(
encoded_question)
merged = add([encoded_context, encoded_question])
encoder_lstm = LSTM(units=hidden_units,
return_state=True,
name='encoder_lstm')
encoder_outputs, encoder_state_h, encoder_state_c = encoder_lstm(merged)
encoder_states = [encoder_state_h, encoder_state_c]
decoder_inputs = Input(shape=(None, num_decoder_tokens),
name='decoder_inputs')
decoder_lstm = LSTM(units=hidden_units,
return_state=True,
return_sequences=True,
name='decoder_lstm')
decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(
decoder_inputs, initial_state=encoder_states)
decoder_dense = Dense(units=num_decoder_tokens,
activation='softmax',
name='decoder_dense')
decoder_outputs = decoder_dense(decoder_outputs)
model = Model([context_inputs, question_inputs, decoder_inputs],
decoder_outputs)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
encoder_model = Model([context_inputs, question_inputs], encoder_states)
decoder_state_inputs = [
Input(shape=(hidden_units, )),
Input(shape=(hidden_units, ))
]
decoder_outputs, state_h, state_c = decoder_lstm(
decoder_inputs, initial_state=decoder_state_inputs)
decoder_states = [state_h, state_c]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model([decoder_inputs] + decoder_state_inputs,
[decoder_outputs] + decoder_states)
return model, encoder_model, decoder_model
def instance_network(shape):
input_img = Input(shape=(*shape, 1, MAX_INSTANCES), name='fg_input_img')
input_packed = Lambda(lambda x: pack_instance(x),
name='fg_pack_input')(input_img)
input_img_3 = Lambda(lambda x: tf.tile(x, [1, 1, 1, 3]),
name='fg_input_tile')(input_packed)
# VGG16 without top layers
VGG_model = applications.vgg16.VGG16(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3))
model_3 = Model(VGG_model.input,
VGG_model.layers[-6].output,
name='fg_model_3')(input_img_3)
# Global features
conv2d_6 = Conv2D(512, (3, 3),
padding='same',
strides=(2, 2),
activation='relu',
name='fg_conv2d_6')(model_3)
batch_normalization_1 = BatchNormalization(
name='fg_batch_normalization_1')(conv2d_6)
conv2d_7 = Conv2D(512, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_7')(batch_normalization_1)
batch_normalization_2 = BatchNormalization(
name='fg_batch_normalization_2')(conv2d_7)
conv2d_8 = Conv2D(512, (3, 3),
padding='same',
strides=(2, 2),
activation='relu',
name='fg_conv2d_8')(batch_normalization_2)
batch_normalization_3 = BatchNormalization(
name='fg_batch_normalization_3')(conv2d_8)
conv2d_9 = Conv2D(512, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_9')(batch_normalization_3)
batch_normalization_4 = BatchNormalization(
name='fg_batch_normalization_4')(conv2d_9)
# Global feature pass back to colorization + classification
flatten_1 = Flatten(name='fg_flatten_1')(batch_normalization_4)
dense_1 = Dense(1024, activation='relu', name='fg_dense_1')(flatten_1)
dense_2 = Dense(512, activation='relu', name='fg_dense_2')(dense_1)
dense_3 = Dense(256, activation='relu', name='fg_dense_3')(dense_2)
repeat_vector_1 = RepeatVector(28 * 28, name='fg_repeat_vector_1')(dense_3)
reshape_1 = Reshape((28, 28, 256), name='fg_reshape_1')(repeat_vector_1)
# Mid-level features
conv2d_10 = Conv2D(512, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_10')(model_3)
batch_normalization_5 = BatchNormalization(
name='fg_batch_normalization_5')(conv2d_10)
conv2d_11 = Conv2D(256, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_11')(batch_normalization_5)
batch_normalization_6 = BatchNormalization(
name='fg_batch_normalization_6')(conv2d_11)
# Fusion of (VGG16 -> Mid-level) + (VGG16 -> Global) + Colorization
concatenate_2 = concatenate([batch_normalization_6, reshape_1],
name='fg_concatenate_2')
conv2d_12 = Conv2D(256, (1, 1),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_12')(concatenate_2)
conv2d_13 = Conv2D(128, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_13')(conv2d_12)
up_sampling2d_1 = UpSampling2D(size=(2, 2),
name='fg_up_sampling2d_1',
interpolation='bilinear')(conv2d_13)
# conv2dt_1 = Conv2DTranspose(64, (4, 4), padding='same', strides=(2, 2), name='fg_conv2dt_1')(conv2d_13)
conv2d_14 = Conv2D(64, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_14')(up_sampling2d_1)
conv2d_15 = Conv2D(64, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_15')(conv2d_14)
up_sampling2d_2 = UpSampling2D(size=(2, 2),
name='fg_up_sampling2d_2',
interpolation='bilinear')(conv2d_15)
# conv2dt_2 = Conv2DTranspose(32, (4, 4), padding='same', strides=(2, 2), name='fg_conv2dt_2')(conv2d_15)
conv2d_16 = Conv2D(32, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='fg_conv2d_16')(up_sampling2d_2)
conv2d_17 = Conv2D(2, (3, 3),
padding='same',
strides=(1, 1),
activation='sigmoid',
name='fg_conv2d_17')(conv2d_16)
# up_sampling2d_3 = UpSampling2D(size=(2, 2), name='fg_up_sampling2d_3')(conv2d_17)
model_3_unpack = Lambda(lambda x: unpack_instance(x),
name='fg_model_3_unpack')(model_3)
conv2d_11_unpack = Lambda(lambda x: unpack_instance(x),
name='fg_conv2d_11_unpack')(conv2d_11)
conv2d_13_unpack = Lambda(lambda x: unpack_instance(x),
name='fg_conv2d_13_unpack')(conv2d_13)
conv2d_15_unpack = Lambda(lambda x: unpack_instance(x),
name='fg_conv2d_15_unpack')(conv2d_15)
conv2d_17_unpack = Lambda(lambda x: unpack_instance(x),
name='fg_conv2d_17_unpack')(conv2d_17)
generated = Model(inputs=input_img,
outputs=[
model_3_unpack, conv2d_11_unpack, conv2d_13_unpack,
conv2d_15_unpack, conv2d_17_unpack
])
return generated
def spread_axis(input, filters, permute_dims):
gather = Reshape((FOUR * filters,))(input)
repeat = RepeatVector(FOUR * FOUR)(gather)
spread = Reshape((FOUR, FOUR, FOUR, filters))(repeat)
permute = Permute(permute_dims)(spread)
return permute
def fusion_network(shape, batch_size):
input_img = Input(shape=(*shape, 1), name='input_img')
input_img_3 = Lambda(lambda x: tf.tile(x, [1, 1, 1, 3]),
name='input_tile')(input_img)
bbox = Input(shape=(4, MAX_INSTANCES), name='bbox')
mask = Input(shape=(*shape, MAX_INSTANCES), name='mask')
# VGG16 without top layers
VGG_model = applications.vgg16.VGG16(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3))
vgg_model_3_pre = Model(VGG_model.input,
VGG_model.layers[-6].output,
name='model_3')(input_img_3)
fg_model_3 = Input(shape=(*vgg_model_3_pre.get_shape().as_list()[1:],
MAX_INSTANCES),
name='fg_model_3') # <-
vgg_model_3 = WeightGenerator(64, batch_size, name='weight_generator_1')(
[fg_model_3, vgg_model_3_pre, bbox, mask]) # <-
# Global features
conv2d_6 = Conv2D(512, (3, 3),
padding='same',
strides=(2, 2),
activation='relu',
name='conv2d_6')(vgg_model_3)
batch_normalization_1 = BatchNormalization(
name='batch_normalization_1')(conv2d_6)
conv2d_7 = Conv2D(512, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_7')(batch_normalization_1)
batch_normalization_2 = BatchNormalization(
name='batch_normalization_2')(conv2d_7)
conv2d_8 = Conv2D(512, (3, 3),
padding='same',
strides=(2, 2),
activation='relu',
name='conv2d_8')(batch_normalization_2)
batch_normalization_3 = BatchNormalization(
name='batch_normalization_3')(conv2d_8)
conv2d_9 = Conv2D(512, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_9')(batch_normalization_3)
batch_normalization_4 = BatchNormalization(
name='batch_normalization_4')(conv2d_9)
# Classification
flatten_2 = Flatten(name='flatten_2')(batch_normalization_4)
dense_4 = Dense(4096, activation='relu', name='dense_4')(flatten_2)
dense_5 = Dense(4096, activation='relu', name='dense_5')(dense_4)
dense_6 = Dense(1000, activation='softmax', name='dense_6')(dense_5)
# Global feature pass back to colorization + classification
flatten_1 = Flatten(name='flatten_1')(batch_normalization_4)
dense_1 = Dense(1024, activation='relu', name='dense_1')(flatten_1)
dense_2 = Dense(512, activation='relu', name='dense_2')(dense_1)
dense_3 = Dense(256, activation='relu', name='dense_3')(dense_2)
repeat_vector_1 = RepeatVector(28 * 28, name='repeat_vector_1')(dense_3)
reshape_1 = Reshape((28, 28, 256), name='reshape_1')(repeat_vector_1)
# Mid-level features
conv2d_10 = Conv2D(512, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_10')(vgg_model_3)
batch_normalization_5 = BatchNormalization(
name='batch_normalization_5')(conv2d_10)
conv2d_11_pre = Conv2D(256, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_11')(batch_normalization_5)
fg_conv2d_11 = Input(shape=(*conv2d_11_pre.get_shape().as_list()[1:],
MAX_INSTANCES),
name='fg_conv2d_11') # <-
conv2d_11 = WeightGenerator(32, batch_size, name='weight_generator_2')(
[fg_conv2d_11, conv2d_11_pre, bbox, mask]) # <-
batch_normalization_6 = BatchNormalization(
name='batch_normalization_6')(conv2d_11)
# Fusion of (VGG16 -> Mid-level) + (VGG16 -> Global) + Colorization
concatenate_2 = concatenate([batch_normalization_6, reshape_1],
name='concatenate_2')
conv2d_12 = Conv2D(256, (1, 1),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_12')(concatenate_2)
conv2d_13_pre = Conv2D(128, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_13')(conv2d_12)
fg_conv2d_13 = Input(shape=(*conv2d_13_pre.get_shape().as_list()[1:],
MAX_INSTANCES),
name='fg_conv2d_13') # <-
conv2d_13 = WeightGenerator(16, batch_size, name='weight_generator_3')(
[fg_conv2d_13, conv2d_13_pre, bbox, mask]) # <-
# conv2dt_1 = Conv2DTranspose(64, (4, 4), padding='same', strides=(2, 2), name='conv2dt_1')(conv2d_13)
up_sampling2d_1 = UpSampling2D(size=(2, 2),
name='up_sampling2d_1',
interpolation='bilinear')(conv2d_13)
conv2d_14 = Conv2D(64, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_14')(up_sampling2d_1)
conv2d_15_pre = Conv2D(64, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_15')(conv2d_14)
fg_conv2d_15 = Input(shape=(*conv2d_15_pre.get_shape().as_list()[1:],
MAX_INSTANCES),
name='fg_conv2d_15') # <-
conv2d_15 = WeightGenerator(16, batch_size, name='weight_generator_4')(
[fg_conv2d_15, conv2d_15_pre, bbox, mask]) # <-
# conv2dt_2 = Conv2DTranspose(32, (4, 4), padding='same', strides=(2, 2), name='conv2dt_2')(conv2d_15)
up_sampling2d_2 = UpSampling2D(size=(2, 2),
name='up_sampling2d_2',
interpolation='bilinear')(conv2d_15)
conv2d_16 = Conv2D(32, (3, 3),
padding='same',
strides=(1, 1),
activation='relu',
name='conv2d_16')(up_sampling2d_2)
conv2d_17_pre = Conv2D(2, (3, 3),
padding='same',
strides=(1, 1),
activation='sigmoid',
name='conv2d_17')(conv2d_16)
fg_conv2d_17 = Input(shape=(*conv2d_17_pre.get_shape().as_list()[1:],
MAX_INSTANCES),
name='fg_conv2d_17') # <-
conv2d_17 = WeightGenerator(16, batch_size, name='weight_generator_5')(
[fg_conv2d_17, conv2d_17_pre, bbox, mask]) # <-
# conv2dt_3 = Conv2DTranspose(2, (4, 4), padding='same', strides=(2, 2), name='conv2dt_3')(conv2d_17)
up_sampling2d_3 = UpSampling2D(size=(2, 2),
name='up_sampling2d_3',
interpolation='bilinear')(conv2d_17)
return Model(inputs=[
input_img, fg_model_3, fg_conv2d_11, fg_conv2d_13, fg_conv2d_15,
fg_conv2d_17, bbox, mask
],
outputs=[up_sampling2d_3, dense_6])
def __init__(self,
lr=0.00017654,
lat_input_shape=(64, ),
screen_input_shape=(
64,
64,
),
structured_input_shape=(2, ),
verbose=False):
"""
https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models
https://keras.io/gett ing-started/functional-api-guide/#shared-layers
https://blog.keras.io/building-autoencoders-in-keras.html
"""
# Gross hack, change later?
self.lr = lr
if verbose:
print("Network structured input shape is",
structured_input.get_shape())
print("Network screen input shape is", screen_input.get_shape())
print("Network latent input shape is", lat_input.get_shape())
# Create the two state encoding legs
structured_input_a = Input(shape=structured_input_shape)
lat_input_a = Input(shape=lat_input_shape)
screen_input_a = Input(shape=screen_input_shape, )
structured_input_b = Input(shape=structured_input_shape)
lat_input_b = Input(shape=lat_input_shape)
screen_input_b = Input(shape=screen_input_shape)
eng_state_a = [structured_input_a, lat_input_a, screen_input_a]
eng_state_b = [structured_input_b, lat_input_b, screen_input_b]
# We want to broadcast the structured input (x, y) into their own
# channels, each with the same dimension as the screen input
# We can then concatenate, then convolve over the whole tensor
x = RepeatVector(64 * 64)(structured_input_a)
x = Reshape((64, 64, 2))(x)
structured_output_a = x
x = RepeatVector(64 * 64)(structured_input_b)
x = Reshape((64, 64, 2))(x)
structured_output_b = x
# Similar with the latent vector, except it will simply be repeated
# column wise
x = RepeatVector(64)(lat_input_a)
x = Reshape((64, 64, 1))(x)
lat_output_a = x
x = RepeatVector(64)(lat_input_b)
x = Reshape((64, 64, 1))(x)
lat_output_b = x
# The screen is the correct shape, just add a channel dimension
x = Reshape((64, 64, 1))(screen_input_a)
screen_output_a = x
x = Reshape((64, 64, 1))(screen_input_b)
screen_output_b = x
x = concatenate([
screen_output_a, structured_output_a, lat_output_a,
screen_output_b, structured_output_b, lat_output_b
],
axis=-1)
print("Hello, World!", x.shape)
x = Conv2D(16, (3, 3))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
print("1", x.shape)
x = Conv2D(32, (3, 3))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(2)(x)
print("2", x.shape)
x = Conv2D(64, (3, 3))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
print("3", x.shape)
x = Conv2D(128, (3, 3))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(2)(x)
print("4", x.shape)
x = Conv2D(256, (3, 3))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
print("5", x.shape)
x = Conv2D(512, (3, 3))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(2)(x)
print("6", x.shape)
x = Conv2D(1024, (3, 3))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
print("7", x.shape)
x = Conv2D(2, (1, 1))(x)
x = Activation('linear')(x)
x = AveragePooling2D()(x)
print("8", x.shape)
x = Activation("softmax")(x)
print("9", x.shape)
prob_output = Reshape((2, ))(x)
print("10", prob_output.shape)
self.probabilityNetwork = Model(inputs=eng_state_a + eng_state_b,
outputs=[prob_output])
def one_hot(x):
x = K.argmax(x)
x = tf.one_hot(x, 78)
x = RepeatVector(1)(x)
return x
def layers(self):
mean_input = Input(self.latent_size,
self.batch_size,
name="mean_input")
stddev_input = Input(self.latent_size,
self.batch_size,
name="stddev_input")
mask_input = Input(self.mask_input_shape,
self.batch_size,
name="mask_input")
detail_input = Input(self.input_shape,
self.batch_size,
name="detail_input")
##########################
# Detail encoder network #
##########################
# 128x128x3
detail_net = Conv2D(filters=16,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_input)
detail_net = BatchNormalization()(detail_net)
detail_net = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = Conv2D(filters=16,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail128x128x16 = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = MaxPooling2D(pool_size=2)(detail128x128x16)
detail_net = Dropout(self.dropout)(detail_net)
# 64x64x16
detail_net = Conv2D(filters=32,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail_net = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = Conv2D(filters=32,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail64x64x32 = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = MaxPooling2D(pool_size=2)(detail64x64x32)
detail_net = Dropout(self.dropout)(detail_net)
# 32x32x32
detail_net = Conv2D(filters=64,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail_net = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = Conv2D(filters=64,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail32x32x64 = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = MaxPooling2D(pool_size=2)(detail32x32x64)
detail_net = Dropout(self.dropout)(detail_net)
# 16x16x64
detail_net = Conv2D(filters=128,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail_net = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = Conv2D(filters=128,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail16x16x128 = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = MaxPooling2D(pool_size=2)(detail16x16x128)
detail_net = Dropout(self.dropout)(detail_net)
# 8x8x128
detail_net = Conv2D(filters=256,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail_net = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = Conv2D(filters=256,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail8x8x256 = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = MaxPooling2D(pool_size=2)(detail8x8x256)
detail_net = Dropout(self.dropout)(detail_net)
# 4x4x256
detail_net = Conv2D(filters=512,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail_net = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = Conv2D(filters=512,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail4x4x512 = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = MaxPooling2D(pool_size=2)(detail4x4x512)
detail_net = Dropout(self.dropout)(detail_net)
# 2x2x512
detail_net = Conv2D(filters=1024,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail_net = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = Conv2D(filters=1024,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same')(detail_net)
detail_net = BatchNormalization()(detail_net)
detail2x2x1024 = LeakyReLU(alpha=self.leak)(detail_net)
detail_net = MaxPooling2D(pool_size=2)(detail2x2x1024)
# 1x1x1024
detail_net = Dropout(self.dropout)(detail_net)
########################
# Mask encoder network #
########################
# {frames}x128x128x3
mask_net = TimeDistributed(
Conv2D(filters=16,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_input)
mask_net = BatchNormalization()(mask_net)
mask_net = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(
Conv2D(filters=16,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask128x128x16 = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(MaxPooling2D(pool_size=2))(mask128x128x16)
mask_net = TimeDistributed(Dropout(self.dropout))(mask_net)
# {frames}x64x64x16
mask_net = TimeDistributed(
Conv2D(filters=32,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask_net = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(
Conv2D(filters=32,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask64x64x32 = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(MaxPooling2D(pool_size=2))(mask64x64x32)
mask_net = TimeDistributed(Dropout(self.dropout))(mask_net)
# {frames}x32x32x32
mask_net = TimeDistributed(
Conv2D(filters=64,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask_net = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(
Conv2D(filters=64,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask32x32x64 = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(MaxPooling2D(pool_size=2))(mask32x32x64)
mask_net = TimeDistributed(Dropout(self.dropout))(mask_net)
# {frames}x16x16x64
mask_net = TimeDistributed(
Conv2D(filters=128,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask_net = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(
Conv2D(filters=128,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask16x16x128 = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(MaxPooling2D(pool_size=2))(mask16x16x128)
mask_net = TimeDistributed(Dropout(self.dropout))(mask_net)
# {frames}x8x8x128
mask_net = TimeDistributed(
Conv2D(filters=256,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask_net = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(
Conv2D(filters=256,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask8x8x256 = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(MaxPooling2D(pool_size=2))(mask8x8x256)
mask_net = TimeDistributed(Dropout(self.dropout))(mask_net)
# {frames}x4x4x256
mask_net = TimeDistributed(
Conv2D(filters=512,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask_net = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(
Conv2D(filters=512,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask4x4x512 = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(MaxPooling2D(pool_size=2))(mask4x4x512)
mask_net = TimeDistributed(Dropout(self.dropout))(mask_net)
# {frames}x2x2x512
mask_net = TimeDistributed(
Conv2D(filters=1024,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask_net = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(
Conv2D(filters=1024,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(mask_net)
mask_net = BatchNormalization()(mask_net)
mask2x2x1024 = TimeDistributed(LeakyReLU(alpha=self.leak))(mask_net)
mask_net = TimeDistributed(MaxPooling2D(pool_size=2))(mask2x2x1024)
# {frames}x1x1x1024
mask_net = TimeDistributed(Dropout(self.dropout))(mask_net)
mask_net = TimeDistributed(Flatten(name="mask_flatten"))(mask_net)
epsilon = TimeDistributed(Dense(self.latent_size),
name="epsilon")(mask_net)
samples = SampleLayer(
beta=self.beta,
capacity=self.latent_size,
epsilon_sequence=True,
name="sampling_layer")([mean_input, stddev_input, epsilon])
###################
# Decoder network #
###################
net = TimeDistributed(Dense(self.latent_size,
activation='relu'))(samples)
# reexpand the input from flat:
net = TimeDistributed(Reshape((1, 1, self.latent_size)))(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x1x1x1024
net = TimeDistributed(
Conv2DTranspose(1024, (3, 3), strides=(2, 2), padding='same'))(net)
# {frames}x2x2x1024
net = concatenate([
net,
Reshape(
(-1, 2, 2,
1024))(RepeatVector(net.shape[1])(Flatten()(detail2x2x1024)))
])
net = TimeDistributed(Dropout(self.dropout))(net)
# {frames}x2x2x2048
net = TimeDistributed(
Conv2D(filters=1024,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x2x2x1024
net = concatenate([net, mask2x2x1024])
net = TimeDistributed(Dropout(self.dropout))(net)
net = TimeDistributed(
Conv2D(filters=1024,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x2x2x1024
net = TimeDistributed(
Conv2DTranspose(512, (3, 3), strides=(2, 2), padding='same'))(net)
# {frames}x4x4x512
net = concatenate([
net,
Reshape(
(-1, 4, 4,
512))(RepeatVector(net.shape[1])(Flatten()(detail4x4x512)))
])
# {frames}x4x4x1024
net = TimeDistributed(Dropout(self.dropout))(net)
net = TimeDistributed(
Conv2D(filters=512,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x4x4x512
net = concatenate([net, mask4x4x512])
# {frames}x4x4x1024
net = TimeDistributed(Dropout(self.dropout))(net)
net = ConvLSTM2D(filters=512,
kernel_size=3,
return_sequences=True,
use_bias=True,
data_format='channels_last',
padding='same')(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x4x4x512
net = TimeDistributed(
Conv2DTranspose(256, (3, 3), strides=(2, 2), padding='same'))(net)
# {frames}x8x8x256
net = concatenate([
net,
Reshape(
(-1, 8, 8,
256))(RepeatVector(net.shape[1])(Flatten()(detail8x8x256)))
])
# {frames}x8x8x512
net = TimeDistributed(Dropout(self.dropout))(net)
net = TimeDistributed(
Conv2D(filters=256,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x8x8x256
net = concatenate([net, mask8x8x256])
net = TimeDistributed(Dropout(self.dropout))(net)
net = ConvLSTM2D(filters=256,
kernel_size=3,
return_sequences=True,
use_bias=True,
data_format='channels_last',
padding='same')(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x8x8x256
net = TimeDistributed(
Conv2DTranspose(128, (3, 3), strides=(2, 2), padding='same'))(net)
# {frames}x16x16x128
net = concatenate([
net,
Reshape(
(-1, 16, 16,
128))(RepeatVector(net.shape[1])(Flatten()(detail16x16x128)))
])
# {frames}x16x16x256
net = TimeDistributed(Dropout(self.dropout))(net)
net = TimeDistributed(
Conv2D(filters=128,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x16x16x128
net = concatenate([net, mask16x16x128])
# {frames}x16x16x256
net = TimeDistributed(Dropout(self.dropout))(net)
net = ConvLSTM2D(filters=128,
kernel_size=3,
return_sequences=True,
use_bias=True,
data_format='channels_last',
padding='same')(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x16x16x128
net = TimeDistributed(
Conv2DTranspose(64, (3, 3), strides=(2, 2), padding='same'))(net)
# {frames}x32x32x64
net = concatenate([
net,
Reshape(
(-1, 32, 32,
64))(RepeatVector(net.shape[1])(Flatten()(detail32x32x64)))
])
# {frames}x32x32x128
net = TimeDistributed(Dropout(self.dropout))(net)
net = TimeDistributed(
Conv2D(filters=64,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x32x32x64
net = concatenate([net, mask32x32x64])
net = TimeDistributed(Dropout(self.dropout))(net)
# {frames}x32x32x128
net = TimeDistributed(
Conv2D(filters=64,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x32x32x64
net = TimeDistributed(
Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same'))(net)
# {frames}x64x64x32
net = concatenate([
net,
Reshape(
(-1, 64, 64,
32))(RepeatVector(net.shape[1])(Flatten()(detail64x64x32)))
])
# {frames}x64x64x64
net = TimeDistributed(Dropout(self.dropout))(net)
net = TimeDistributed(
Conv2D(filters=32,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x64x64x32
net = concatenate([net, mask64x64x32])
net = TimeDistributed(Dropout(self.dropout))(net)
# {frames}x64x64x64
net = TimeDistributed(
Conv2D(filters=32,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x64x64x32
net = TimeDistributed(
Conv2DTranspose(16, (3, 3), strides=(2, 2), padding='same'))(net)
# {frames}x128x128x16
net = concatenate([
net,
Reshape(
(-1, 128, 128,
16))(RepeatVector(net.shape[1])(Flatten()(detail128x128x16)))
])
# {frames}x128x128x32
net = TimeDistributed(Dropout(self.dropout))(net)
net = TimeDistributed(
Conv2D(filters=16,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x128x128x16
net = concatenate([net, mask128x128x16])
# {frames}x128x128x32
net = TimeDistributed(
Conv2D(filters=16,
kernel_size=3,
use_bias=True,
data_format='channels_last',
padding='same'))(net)
net = BatchNormalization()(net)
net = TimeDistributed(LeakyReLU(alpha=self.leak))(net)
# {frames}x128x128x16
net = ConvLSTM2D(filters=self.input_shape[-1],
kernel_size=(1, 1),
return_sequences=False,
padding='same')(net)
# 128x128x3
return [mean_input, stddev_input, detail_input, mask_input], net