def build(self):
d_input = Input(shape=(2, ))
x = Dense(64, activation='relu')(d_input)
x = Dropout(0.5)(x)
x = Dense(64, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(64, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(64, activation='relu')(x)
x = Dropout(0.5)(x)
d_output = Dense(3, activation="relu")(x)
model = Model(inputs=[d_input], outputs=[d_output])
optimizer = Adam(lr=0.00005)
model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])
return model
def run(model):
# Download kitti dataset
helper.maybe_download_training_img(DATA_DIRECTORY)
x, y = helper.get_data(TRAINING_DATA_DIRECTORY, IMAGE_SHAPE)
if model is None:
inputs = Input(shape=(IMAGE_SHAPE[0], IMAGE_SHAPE[1], 3))
# Block 1
block1_conv1 = Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(inputs)
block1_conv2 = Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(block1_conv1)
block1_pool = MaxPooling2D((2, 2), strides=(2, 2),
name='block1_pool')(block1_conv2)
# Block 2
block2_conv1 = Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(block1_pool)
block2_conv2 = Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(block2_conv1)
block2_pool = MaxPooling2D((2, 2), strides=(2, 2),
name='block2_pool')(block2_conv2)
# Block 3
block3_conv1 = Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(block2_pool)
block3_conv2 = Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(block3_conv1)
block3_conv3 = Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(block3_conv2)
block3_pool = MaxPooling2D((2, 2), strides=(2, 2),
name='block3_pool')(block3_conv3)
# Block 4
block4_conv1 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(block3_pool)
block4_conv2 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(block4_conv1)
block4_conv3 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(block4_conv2)
block4_pool = MaxPooling2D((2, 2), strides=(2, 2),
name='block4_pool')(block4_conv3)
# Block 5
block5_conv1 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(block4_pool)
block5_conv2 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(block5_conv1)
block5_conv3 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(block5_conv2)
block5_pool = MaxPooling2D((2, 2), strides=(2, 2),
name='block5_pool')(block5_conv3)
pool5_conv1x1 = Conv2D(2, (1, 1), activation='relu',
padding='same')(block5_pool)
upsample_1 = Conv2DTranspose(2,
kernel_size=(4, 4),
strides=(2, 2),
padding="same")(pool5_conv1x1)
pool4_conv1x1 = Conv2D(2, (1, 1), activation='relu',
padding='same')(block4_pool)
add_1 = Add()([upsample_1, pool4_conv1x1])
upsample_2 = Conv2DTranspose(2,
kernel_size=(4, 4),
strides=(2, 2),
padding="same")(add_1)
pool3_conv1x1 = Conv2D(2, (1, 1), activation='relu',
padding='same')(block3_pool)
add_2 = Add()([upsample_2, pool3_conv1x1])
upsample_3 = Conv2DTranspose(2,
kernel_size=(16, 16),
strides=(8, 8),
padding="same")(add_2)
output = Dense(2, activation='softmax')(upsample_3)
model = Model(inputs, output, name='multinet_seg')
adam = Adam(lr=LEARNING_RATE)
model.compile(loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])
model.fit(x, y, batch_size=BATCH_SIZE, epochs=EPOCHS)
model.save('trained_model' + str(time.time()) + '.h5')
def add(self, layer):
"""Adds a layer instance on top of the layer stack.
Arguments:
layer: layer instance.
Raises:
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (forbidden in `Sequential` models).
"""
if not isinstance(layer, Layer):
raise TypeError('The added layer must be '
'an instance of class Layer. '
'Found: ' + str(layer))
if not self.outputs:
# first layer in model: check that it is an input layer
if not layer.inbound_nodes:
# create an input layer
if not hasattr(layer, 'batch_input_shape'):
raise ValueError('The first layer in a '
'Sequential model must '
'get an `input_shape` or '
'`batch_input_shape` argument.')
# Instantiate the input layer.
x = Input(batch_shape=layer.batch_input_shape,
dtype=layer.dtype,
name=layer.name + '_input')
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
if len(layer.inbound_nodes) != 1:
raise ValueError('A layer added to a Sequential model must '
'not already be connected somewhere else. '
'Model received layer ' + layer.name +
' which has ' +
str(len(layer.inbound_nodes)) +
' pre-existing inbound connections.')
if len(layer.inbound_nodes[0].output_tensors) != 1:
raise ValueError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
self.outputs = [layer.inbound_nodes[0].output_tensors[0]]
self.inputs = topology.get_source_inputs(self.outputs[0])
# We create an input node, which we will keep updated
# as we add more layers
topology.Node(
outbound_layer=self,
inbound_layers=[],
node_indices=[],
tensor_indices=[],
input_tensors=self.inputs,
output_tensors=self.outputs,
# no model-level masking for now
input_masks=[None for _ in self.inputs],
output_masks=[None])
else:
output_tensor = layer(self.outputs[0])
if isinstance(output_tensor, list):
raise TypeError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
self.outputs = [output_tensor]
# update self.inbound_nodes
self.inbound_nodes[0].output_tensors = self.outputs
self.inbound_nodes[0].output_shapes = [
K.int_shape(self.outputs[0])
]
self.layers.append(layer)
self.built = False