def get_unet():
concat_axis = 3
#inputs = layers.Input(shape = (256, 256, 2))
inputs = layers.Input(shape=(1024, 1024, 4))
#inputs = layers.Input(shape = (2048,2048,4))
feats = 16
bn0 = BatchNormalization(axis=3)(inputs)
conv1 = layers.Conv2D(feats, (3, 3),
activation='relu',
padding='same',
name='conv1_1')(bn0)
bn1 = BatchNormalization(axis=3)(conv1)
conv1 = layers.Conv2D(feats, (3, 3), activation='relu',
padding='same')(bn1)
bn2 = BatchNormalization(axis=3)(conv1)
pool1 = layers.MaxPooling2D(pool_size=(2, 2))(bn2)
conv2 = layers.Conv2D(2 * feats, (3, 3), activation='relu',
padding='same')(pool1)
bn3 = BatchNormalization(axis=3)(conv2)
conv2 = layers.Conv2D(2 * feats, (3, 3), activation='relu',
padding='same')(bn3)
bn4 = BatchNormalization(axis=3)(conv2)
pool2 = layers.MaxPooling2D(pool_size=(2, 2))(bn4)
conv3 = layers.Conv2D(4 * feats, (3, 3), activation='relu',
padding='same')(pool2)
bn5 = BatchNormalization(axis=3)(conv3)
conv3 = layers.Conv2D(4 * feats, (3, 3), activation='relu',
padding='same')(bn5)
bn6 = BatchNormalization(axis=3)(conv3)
pool3 = layers.MaxPooling2D(pool_size=(2, 2))(bn6)
conv4 = layers.Conv2D(8 * feats, (3, 3), activation='relu',
padding='same')(pool3)
bn7 = BatchNormalization(axis=3)(conv4)
conv4 = layers.Conv2D(8 * feats, (3, 3), activation='relu',
padding='same')(bn7)
bn8 = BatchNormalization(axis=3)(conv4)
pool4 = layers.MaxPooling2D(pool_size=(2, 2))(bn8)
conv5 = layers.Conv2D(16 * feats, (3, 3),
activation='relu',
padding='same')(pool4)
bn9 = BatchNormalization(axis=3)(conv5)
conv5 = layers.Conv2D(16 * feats, (3, 3),
activation='relu',
padding='same')(bn9)
bn10 = BatchNormalization(axis=3)(conv5)
pool5 = layers.MaxPooling2D(pool_size=(2, 2))(bn10)
conv6 = layers.Conv2D(32 * feats, (3, 3),
activation='relu',
padding='same')(pool5)
bn11 = BatchNormalization(axis=3)(conv6)
conv6 = layers.Conv2D(32 * feats, (3, 3),
activation='relu',
padding='same')(bn11)
bn12 = BatchNormalization(axis=3)(conv6)
up_conv6 = layers.UpSampling2D(size=(2, 2))(bn12)
up7 = layers.concatenate([up_conv6, conv5], axis=concat_axis)
conv7 = layers.Conv2D(16 * feats, (3, 3),
activation='relu',
padding='same')(up7)
bn13 = BatchNormalization(axis=3)(conv6)
conv7 = layers.Conv2D(16 * feats, (3, 3),
activation='relu',
padding='same')(bn12)
bn14 = BatchNormalization(axis=3)(conv6)
up_conv5 = layers.UpSampling2D(size=(2, 2))(bn10)
up6 = layers.concatenate([up_conv5, conv4], axis=concat_axis)
conv6 = layers.Conv2D(8 * feats, (3, 3), activation='relu',
padding='same')(up6)
bn15 = BatchNormalization(axis=3)(conv6)
conv6 = layers.Conv2D(8 * feats, (3, 3), activation='relu',
padding='same')(bn15)
bn16 = BatchNormalization(axis=3)(conv6)
up_conv6 = layers.UpSampling2D(size=(2, 2))(bn16)
up7 = layers.concatenate([up_conv6, conv3], axis=concat_axis)
conv7 = layers.Conv2D(4 * feats, (3, 3), activation='relu',
padding='same')(up7)
bn13 = BatchNormalization(axis=3)(conv7)
conv7 = layers.Conv2D(4 * feats, (3, 3), activation='relu',
padding='same')(bn13)
bn14 = BatchNormalization(axis=3)(conv7)
up_conv7 = layers.UpSampling2D(size=(2, 2))(bn14)
up8 = layers.concatenate([up_conv7, conv2], axis=concat_axis)
conv8 = layers.Conv2D(2 * feats, (3, 3), activation='relu',
padding='same')(up8)
bn15 = BatchNormalization(axis=3)(conv8)
conv8 = layers.Conv2D(2 * feats, (3, 3), activation='relu',
padding='same')(bn15)
bn16 = BatchNormalization(axis=3)(conv8)
up_conv8 = layers.UpSampling2D(size=(2, 2))(bn16)
up9 = layers.concatenate([up_conv8, conv1], axis=concat_axis)
conv9 = layers.Conv2D(feats, (3, 3), activation='relu',
padding='same')(up9)
bn17 = BatchNormalization(axis=3)(conv9)
conv9 = layers.Conv2D(feats, (3, 3), activation='relu',
padding='same')(bn17)
bn18 = BatchNormalization(axis=3)(conv9)
conv10 = layers.Conv2D(1, (1, 1))(bn18)
#bn19 = BatchNormalization(axis=3)(conv10)
model = models.Model(inputs=inputs, outputs=conv10)
return model
val_ds = val_ds.prefetch(buffer_size=AUTOTUNE)
nomalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
nomalized_ds = train_ds.map(lambda x, y: (nomalization_layer(x), y))
image_batch, labels_batch = next(iter(nomalized_ds))
first_img = image_batch[0]
num_classes = 5
model = Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
layers.experimental.preprocessing.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
layers.experimental.preprocessing.RandomRotation(0.1),
layers.experimental.preprocessing.RandomZoom(0.1),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(),
layers.Dropout(0.2),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(32, kernel_regularizer=keras.regularizers.l2(0.001), activation='relu'),
layers.Dense(16, activation='relu'),
layers.Dense(num_classes)
])
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
train1 = unpickle('./data_batch_1')
x_train = train1[b'data'].reshape(10000, 32, 32, 3)
y_train = train1[b'labels']
y_train = np.array(y_train, dtype='uint8')
y_train = np.expand_dims(y_train, axis=1)
x_train = x_train / 255.0
inputs = tf.keras.Input(shape=(32, 32, 3))
x = inputs
x = layers.Conv2D(16, 5, activation='relu', padding='same')(x)
x = layers.MaxPooling2D(2, 2)(x)
x = layers.Conv2D(16, 5, activation='relu', padding='same')(x)
x = layers.Flatten()(x)
x = layers.Dense(16)(x)
x = layers.Dense(10, activation='softmax')(x)
outputs = x
model4_1 = tf.keras.Model(inputs, outputs)
model4_1.summary()
''' 모델 조정 '''
inputs = tf.keras.Input(shape=(32, 32, 3))
x2 = inputs
x2 = layers.Conv2D(32, 3, activation='relu', padding='valid')(x2)
x2 = layers.MaxPooling2D(2, 2)(x2)
x2 = layers.Conv2D(12, 3, activation='relu', padding='valid')(x2)
x2 = layers.Flatten()(x2)
def vgg_encoder(input_shape=(224, 224, 3)):
image_input = layers.Input(shape=input_shape)
# image = model_input[:,:,:,:3]
# mask = model_input[:,:,:,3]
# Block 1
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(image_input)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
f1 = x
# Block 2
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
f2 = x
# Block 3
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
f3 = x
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
f4 = x
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
f5 = x
return image_input, [f1, f2, f3, f4, f5]
for i in range(25):
plt.subplot(5, 5, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i])
plt.xlabel(class_names[train_labels[i][0]])
plt.show()
train_images = train_images / 255.0
test_images = test_images / 255.0
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels)
def __init__(self, input_shape,embed_size=256):
"""
:param input_shape: [32, 32, 3]
"""
super(VGG16, self).__init__()
weight_decay = 0.000
self.num_classes = embed_size
model = models.Sequential()
model.add(layers.Conv2D(64, (3, 3), padding='same',
input_shape=input_shape, kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.3))
model.add(layers.Conv2D(64, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(128, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(256, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.4))
model.add(layers.Conv2D(512, (3, 3), padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Dropout(0.5))
model.add(layers.Flatten())
model.add(layers.Dense(512,kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Activation('relu'))
model.add(layers.BatchNormalization())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(self.num_classes))
# model.add(layers.Activation('softmax'))
self.model = model
#build a CNN
dense = keras.layers.Dense(units=16)
# Let's say we expect our inputs to be RGB images of arbitrary size
inputs = keras.Input(shape=(None, None, 3))
from tensorflow.keras import layers
# Center-crop images to 101 x 200 to fit sample size
x = CenterCrop(height=101, width=200)(inputs)
# Rescale images to [0, 1]
x = Rescaling(scale=1. / 255)(x)
# Apply some convolution and pooling layers
x = layers.Conv2D(filters=32,
kernel_size=(3, 3),
padding='SAME',
activation='relu')(x)
x = layers.MaxPooling2D(pool_size=(3, 3))(x)
x = layers.Conv2D(filters=32,
kernel_size=(3, 3),
padding='SAME',
activation='relu')(x)
# Apply global average pooling to get flat feature vectors
x = layers.GlobalAveragePooling2D()(x)
# add a dense layer
x = layers.Dense(20, activation='relu')(x)
# Add a dense classifier on top
num_classes = 10
outputs = layers.Dense(num_classes, activation='softmax')(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.summary()
#compile and keep metrics
model.compile(optimizer='adam',
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import datasets
(train_x, train_y), (test_x, test_y) = datasets.mnist.load_data()
inputs = layers.Input((28, 28, 1))
net = layers.Conv2D(32, (3, 3), padding='SAME')(inputs)
net = layers.Activation('relu')(net)
net = layers.Conv2D(32, (3, 3), padding='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.MaxPooling2D(pool_size=(2, 2))(net)
net = layers.Dropout(0.25)(net)
net = layers.Conv2D(64, (3, 3), padding='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.Conv2D(64, (3, 3), padding='SAME')(net)
net = layers.Activation('relu')(net)
net = layers.MaxPooling2D(pool_size=(2, 2))(net)
net = layers.Dropout(0.25)(net)
net = layers.Flatten()(net)
net = layers.Dense(512)(net)
net = layers.Activation('relu')(net)
net = layers.Dropout(0.5)(net)
net = layers.Dense(10)(net) # num_classes
net = layers.Activation('softmax')(net)
model = tf.keras.Model(inputs=inputs, outputs=net, name='Basic_CNN')
def build_BiFPN(features, num_channels, id, freeze_bn=False):
if id == 0:
_, _, C3, C4, C5 = features
P3_in = ConvBlock(num_channels,
kernel_size=1,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_P3'.format(id))(C3)
P4_in = ConvBlock(num_channels,
kernel_size=1,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_P4'.format(id))(C4)
P5_in = ConvBlock(num_channels,
kernel_size=1,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_P5'.format(id))(C5)
# P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
# C5)
# P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
# P6_in)
else:
P3_in, P4_in, P5_in = features
P3_in = ConvBlock(num_channels,
kernel_size=1,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_P3'.format(id))(P3_in)
P4_in = ConvBlock(num_channels,
kernel_size=1,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_P4'.format(id))(P4_in)
P5_in = ConvBlock(num_channels,
kernel_size=1,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_P5'.format(id))(P5_in)
# P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
# P6_in)
# P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
# P7_in)
# upsample
# P7_U = layers.UpSampling2D()(P7_in)
# P6_td = layers.Add()([P7_U, P6_in])
# P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
# P6_U = layers.UpSampling2D()(P6_in)
# P5_td = layers.Add()([P6_U, P5_in])
# P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
P5_U = layers.UpSampling2D()(P5_in)
P4_td = layers.Add()([P5_U, P4_in])
P4_td = DepthwiseConvBlock(kernel_size=3,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_U_P4'.format(id))(P4_td)
P4_U = layers.UpSampling2D()(P4_td)
P3_out = layers.Add()([P4_U, P3_in])
P3_out = DepthwiseConvBlock(kernel_size=3,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_U_P3'.format(id))(P3_out)
# downsample
P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
P4_out = layers.Add()([P3_D, P4_td, P4_in])
P4_out = DepthwiseConvBlock(kernel_size=3,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_D_P4'.format(id))(P4_out)
P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
P5_out = layers.Add()([P4_D, P5_in])
P5_out = DepthwiseConvBlock(kernel_size=3,
strides=1,
freeze_bn=freeze_bn,
name='BiFPN_{}_D_P5'.format(id))(P5_out)
# P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
# P6_out = layers.Add()([P5_D, P6_in])
# P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
# P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
# P7_out = layers.Add()([P6_D, P7_in])
# P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)
return P3_out, P4_out, P5_out #, P6_out , P7_out
def encoder_block_h2(input_tensor, num_filters):
encoder = conv_block_h2(input_tensor, num_filters)
encoder_pool = layers.MaxPooling2D((2, 2), strides=(2, 2))(encoder)
return encoder_pool, encoder
def run(l, epochs=5):
[n, i] = l
loss_list = []
acc_list = []
model = models.Sequential()
model.add(
layers.Conv2D(n, (3, 3),
activation='relu',
input_shape=train_images[0].shape,
padding="same"))
model.add(layers.MaxPooling2D((2, 2)))
model.add(
layers.Conv2D(n, (3, 3),
activation='relu',
input_shape=train_images[0].shape))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(10))
def scheduler(epoch):
if epoch < 2:
return 0.01
else:
return 0.01 * np.cos(np.pi / 2 * (epoch - 2) / (epochs - 2))
callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
#Note that we're not using the scheduler defined above. The scheduler has been left in the code in case the reader
#wishes to use it.
history = model.fit(train_images,
train_labels,
epochs=epochs,
validation_data=(test_images, test_labels),
verbose=0)
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
train_loss, train_acc = model.evaluate(train_images,
train_labels,
verbose=2)
N = model.count_params()
model.save('data/models/model' + str(n) + '_' + str(i))
np.savetxt('data/loss_history/train_loss_hist' + str(n) + '_' + str(i),
history.history['loss'])
np.savetxt('data/acc_history/train_acc_hist' + str(n) + '_' + str(i),
history.history['accuracy'])
np.savetxt('data/loss_history/test_loss_hist' + str(n) + '_' + str(i),
history.history['val_loss'])
np.savetxt('data/acc_history/test_acc_hist' + str(n) + '_' + str(i),
history.history['val_accuracy'])
np.savetxt('data/losses/train_loss' + str(n) + '_' + str(i), [train_loss])
np.savetxt('data/accuracies/train_acc' + str(n) + '_' + str(i),
[train_acc])
np.savetxt('data/losses/test_loss' + str(n) + '_' + str(i), [test_loss])
np.savetxt('data/accuracies/test_acc' + str(n) + '_' + str(i), [test_acc])
return N
x = layers.Conv2D(np.floor(num_filters * alpha),
kernel_size=kernel_size,
strides=strides,
use_bias=False,
padding='same')(x)
x = layers.BatchNormalization(momentum=0.9997)(x)
x = layers.Activation('relu')(x)
return x
if base_model == "FLNet":
# Instantiate ResNet model
inputs = keras.Input(shape=(32, 32, 3))
x = layers.Conv2D(32, 3, activation='relu')(inputs)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPooling2D(3)(x)
num_res_net_blocks = 8
for i in range(num_res_net_blocks):
x = res_net_block(x, 64, 3)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(256, activation='relu')(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(10, activation='softmax')(x)
model = keras.Model(inputs, outputs)
model.save_weights("./chkpts/init_resnet8.ckpt")
if base_model == "lenet":
# Instantiate LeNet model
inputs = keras.Input(shape=(32, 32, 3), name="input")
def tf_keras_model_fn(features, labels, mode, params):
model = tf.keras.Sequential([
layers.Reshape((28, 28, 1), input_shape=(784, )),
layers.Conv2D(64,
3,
padding='same',
data_format='channels_last',
activation='relu'),
layers.MaxPooling2D(pool_size=3,
strides=2,
padding='same',
data_format='channels_last'),
layers.Conv2D(128,
3,
padding='same',
data_format='channels_last',
activation='relu'),
layers.MaxPooling2D(pool_size=3,
strides=2,
padding='same',
data_format='channels_last'),
layers.Conv2D(256,
3,
padding='same',
data_format='channels_last',
activation='relu'),
layers.MaxPooling2D(pool_size=3,
strides=2,
padding='same',
data_format='channels_last'),
layers.Flatten(),
layers.Dense(1024, activation='relu'),
layers.Dense(10) # 在交叉熵损失中做了 softmax 概率归一化，所以此次就不用 softmax 激活了
])
if mode == tf.estimator.ModeKeys.PREDICT:
output = model(features, training=False)
predicted_classes = tf.argmax(output, axis=1)
return tf.estimator.EstimatorSpec(mode, predictions=predicted_classes)
output = model(features, training=True)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels,
logits=output)
loss = tf.reduce_mean(cross_entropy)
if mode == tf.estimator.ModeKeys.TRAIN:
# 选择优化算法很重要
optimizer = tf.train.AdamOptimizer(1e-3)
train_op = optimizer.minimize(loss,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op)
if mode == tf.estimator.ModeKeys.EVAL:
# 定义准确率
predicted_classes = tf.argmax(output, axis=1)
accuracy = tf.metrics.accuracy(labels=tf.argmax(labels, axis=1),
predictions=predicted_classes)
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops={'accuracy': accuracy})
def Unet():
concat_axis = 3
ref_input = layers.Input(shape=(1024, 1024, 2))
gpm_input = layers.Input(shape=(512, 512, 1))
feats = 16
bn0 = layers.BatchNormalization(axis=3)(ref_input)
conv1 = layers.Conv2D(feats, (3, 3),
activation='relu',
padding='same',
name='conv1_1')(bn0)
bn1 = layers.BatchNormalization(axis=3)(conv1)
conv1 = layers.Conv2D(feats, (3, 3), activation='relu',
padding='same')(bn1)
bn2 = layers.BatchNormalization(axis=3)(conv1)
pool1 = layers.MaxPooling2D(pool_size=(2, 2))(bn2)
gpmadd = layers.concatenate([gpm_input, pool1], axis=concat_axis)
gpmaddn = layers.BatchNormalization(axis=3)(gpmadd)
conv2 = layers.Conv2D(2 * feats, (3, 3), activation='relu',
padding='same')(gpmaddn)
bn3 = layers.BatchNormalization(axis=3)(conv2)
conv2 = layers.Conv2D(2 * feats, (3, 3), activation='relu',
padding='same')(bn3)
bn4 = layers.BatchNormalization(axis=3)(conv2)
pool2 = layers.MaxPooling2D(pool_size=(2, 2))(bn4)
conv3 = layers.Conv2D(4 * feats, (3, 3), activation='relu',
padding='same')(pool2)
bn5 = layers.BatchNormalization(axis=3)(conv3)
conv3 = layers.Conv2D(4 * feats, (3, 3), activation='relu',
padding='same')(bn5)
bn6 = layers.BatchNormalization(axis=3)(conv3)
pool3 = layers.MaxPooling2D(pool_size=(2, 2))(bn6)
conv4 = layers.Conv2D(8 * feats, (3, 3), activation='relu',
padding='same')(pool3)
bn7 = layers.BatchNormalization(axis=3)(conv4)
conv4 = layers.Conv2D(8 * feats, (3, 3), activation='relu',
padding='same')(bn7)
bn8 = layers.BatchNormalization(axis=3)(conv4)
pool4 = layers.MaxPooling2D(pool_size=(2, 2))(bn8)
conv5 = layers.Conv2D(16 * feats, (3, 3),
activation='relu',
padding='same')(pool4)
bn9 = layers.BatchNormalization(axis=3)(conv5)
conv5 = layers.Conv2D(16 * feats, (3, 3),
activation='relu',
padding='same')(bn9)
bn10 = layers.BatchNormalization(axis=3)(conv5)
pool5 = layers.MaxPooling2D(pool_size=(2, 2))(bn10)
conv6 = layers.Conv2D(32 * feats, (3, 3),
activation='relu',
padding='same')(pool5)
bn11 = layers.BatchNormalization(axis=3)(conv6)
conv6 = layers.Conv2D(32 * feats, (3, 3),
activation='relu',
padding='same')(bn11)
bn12 = layers.BatchNormalization(axis=3)(conv6)
up_conv6 = layers.UpSampling2D(size=(2, 2))(bn12)
up7 = layers.concatenate([up_conv6, conv5], axis=concat_axis)
conv7 = layers.Conv2D(16 * feats, (3, 3),
activation='relu',
padding='same')(up7)
bn13 = layers.BatchNormalization(axis=3)(conv6)
conv7 = layers.Conv2D(16 * feats, (3, 3),
activation='relu',
padding='same')(bn12)
bn14 = layers.BatchNormalization(axis=3)(conv6)
up_conv5 = layers.UpSampling2D(size=(2, 2))(bn10)
up6 = layers.concatenate([up_conv5, conv4], axis=concat_axis)
conv6 = layers.Conv2D(8 * feats, (3, 3), activation='relu',
padding='same')(up6)
bn15 = layers.BatchNormalization(axis=3)(conv6)
conv6 = layers.Conv2D(8 * feats, (3, 3), activation='relu',
padding='same')(bn15)
bn16 = layers.BatchNormalization(axis=3)(conv6)
up_conv6 = layers.UpSampling2D(size=(2, 2))(bn16)
up7 = layers.concatenate([up_conv6, conv3], axis=concat_axis)
conv7 = layers.Conv2D(4 * feats, (3, 3), activation='relu',
padding='same')(up7)
bn13 = layers.BatchNormalization(axis=3)(conv7)
conv7 = layers.Conv2D(4 * feats, (3, 3), activation='relu',
padding='same')(bn13)
bn14 = layers.BatchNormalization(axis=3)(conv7)
up_conv7 = layers.UpSampling2D(size=(2, 2))(bn14)
up8 = layers.concatenate([up_conv7, conv2], axis=concat_axis)
conv8 = layers.Conv2D(2 * feats, (3, 3), activation='relu',
padding='same')(up8)
bn15 = layers.BatchNormalization(axis=3)(conv8)
conv8 = layers.Conv2D(2 * feats, (3, 3), activation='relu',
padding='same')(bn15)
bn16 = layers.BatchNormalization(axis=3)(conv8)
up_conv8 = layers.UpSampling2D(size=(2, 2))(bn16)
up9 = layers.concatenate([up_conv8, conv1], axis=concat_axis)
conv9 = layers.Conv2D(feats, (3, 3), activation='relu',
padding='same')(up9)
bn17 = layers.BatchNormalization(axis=3)(conv9)
conv9 = layers.Conv2D(feats, (3, 3), activation='relu',
padding='same')(bn17)
bn18 = layers.BatchNormalization(axis=3)(conv9)
conv10 = layers.Conv2D(1, (1, 1), activation='relu')(bn18)
#bn19 = BatchNormalization(axis=3)(conv10)
model = tf.keras.models.Model(inputs=[ref_input, gpm_input],
outputs=conv10)
return model
X_train = X_train.astype('float64')
#%%
numVal = 10000
X_val = X_train[0:numVal, :]
Y_val = Y_train[0:numVal:]
X_train_partial = X_train[numVal:, :]
Y_train_partial = Y_train[numVal:]
"""**Creating the CNN**"""
network = models.Sequential()
network.add(
layers.Conv2D(64, (3, 3), activation='relu', input_shape=(32, 32, 3)))
network.add(layers.Conv2D(64, (3, 3), activation='relu'))
network.add(layers.MaxPooling2D((2, 2)))
network.add(layers.Conv2D(64, (3, 3), activation='relu'))
network.add(layers.Dense(64, activation='relu'))
network.add(layers.MaxPooling2D((2, 2)))
network.add(layers.MaxPooling2D((2, 2)))
network.add(layers.Conv2D(64, (2, 2), activation='relu'))
network.add(layers.Conv2D(64, (1, 1), activation='relu'))
network.add(layers.Dense(64, activation='relu'))
network.add(layers.MaxPooling2D((1, 1)))
network.add(layers.Flatten())
network.add(layers.Dense(64, activation='relu'))
network.add(layers.Dense(10, activation='softmax'))
network.compile(optimizer='Adam',
loss='categorical_crossentropy',
# specify parameters
modelname = 'CNN_sum_K-32-32-64-128_KS-37-37-37-37_MP-12-22-22-32_DO-2-2-2-2-2_AD'
time_sound = 750 # input dimension 1 (time)
nfreqs = 99 # input dimension 2 (frequencies)
#------------------------------------------------------------------------------
# Define model architecture
#------------------------------------------------------------------------------
# CNN 1 - left channel
in1 = layers.Input(
shape=(time_sound, nfreqs,
1)) # define input (rows, columns, channels (only one in my case))
model_l_conv1 = layers.Conv2D(32, (3, 7), activation='relu', padding='same')(
in1) # define first layer and input to the layer
model_l_conv1_mp = layers.MaxPooling2D(pool_size=(1, 2))(model_l_conv1)
model_l_conv1_mp_do = layers.Dropout(0.2)(model_l_conv1_mp)
# CNN 1 - right channel
in2 = layers.Input(shape=(time_sound, nfreqs, 1)) # define input
model_r_conv1 = layers.Conv2D(32, (3, 7), activation='relu', padding='same')(
in2) # define first layer and input to the layer
model_r_conv1_mp = layers.MaxPooling2D(pool_size=(1, 2))(model_r_conv1)
model_r_conv1_mp_do = layers.Dropout(0.2)(model_r_conv1_mp)
# CNN 2 - merged
model_final_merge = layers.Add()([model_l_conv1_mp_do, model_r_conv1_mp_do])
model_final_conv1 = layers.Conv2D(32, (3, 7),
activation='relu',
padding='same')(model_final_merge)
model_final_conv1_mp = layers.MaxPooling2D(pool_size=(2, 2))(model_final_conv1)
def resnet_graph(input_image, architecture, stage5=False, train_bn=True):
"""Build a ResNet graph.
architecture: Can be resnet50 or resnet101
stage5: Boolean. If False, stage5 of the network is not created
train_bn: Boolean. Train or freeze Batch Norm layers
"""
assert architecture in ["resnet50", "resnet101"]
# Stage 1
x = KL.ZeroPadding2D((3, 3))(input_image)
x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)
x = BatchNorm(name='bn_conv1')(x, training=train_bn)
x = KL.Activation('relu')(x)
C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
# Stage 2
x = conv_block(x,
3, [64, 64, 256],
stage=2,
block='a',
strides=(1, 1),
train_bn=train_bn)
x = identity_block(x,
3, [64, 64, 256],
stage=2,
block='b',
train_bn=train_bn)
C2 = x = identity_block(x,
3, [64, 64, 256],
stage=2,
block='c',
train_bn=train_bn)
# Stage 3
x = conv_block(x,
3, [128, 128, 512],
stage=3,
block='a',
train_bn=train_bn)
x = identity_block(x,
3, [128, 128, 512],
stage=3,
block='b',
train_bn=train_bn)
x = identity_block(x,
3, [128, 128, 512],
stage=3,
block='c',
train_bn=train_bn)
C3 = x = identity_block(x,
3, [128, 128, 512],
stage=3,
block='d',
train_bn=train_bn)
# Stage 4
x = conv_block(x,
3, [256, 256, 1024],
stage=4,
block='a',
train_bn=train_bn)
block_count = {"resnet50": 5, "resnet101": 22}[architecture]
for i in range(block_count):
x = identity_block(x,
3, [256, 256, 1024],
stage=4,
block=chr(98 + i),
train_bn=train_bn)
C4 = x
# Stage 5
if stage5:
x = conv_block(x,
3, [512, 512, 2048],
stage=5,
block='a',
train_bn=train_bn)
x = identity_block(x,
3, [512, 512, 2048],
stage=5,
block='b',
train_bn=train_bn)
C5 = x = identity_block(x,
3, [512, 512, 2048],
stage=5,
block='c',
train_bn=train_bn)
else:
C5 = None
return [C1, C2, C3, C4, C5]
def generator_model():
inputs = tf.keras.layers.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, CHANNEL+2))
x = inputs
'''
#generator
x = layers.Conv2D(64, (9, 9), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (9, 9), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same')(x)
res1 = x
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same')(x)
res2 = x
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same')(x)
res3 = x
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same')(x)
#x = layers.add([x, res3])
x = layers.LeakyReLU()(x)
x = layers.add([x, res3])
x = layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same')(x)
#x = layers.add([x, res2])
x = layers.LeakyReLU()(x)
x = layers.add([x, res2])
x = layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same')(x)
#x = layers.subtract([res1, x])
x = layers.LeakyReLU()(x)
x = layers.subtract([res1, x])
x = layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (9, 9), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(3, (9, 9), strides=(1, 1), padding='same')(x)
x = layers.Activation('tanh')(x)
'''
#generator
x = layers.Conv2D(64, (5, 5), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
res1 = x
x = layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
res2 = x
x = layers.Conv2D(128, (3, 3), strides=(2, 2), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
#dilated convs
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same', dilation_rate=2)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same', dilation_rate=4)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(256, (3, 3), strides=(1, 1), padding='same', dilation_rate=8)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same', dilation_rate=16)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same')(x)
x = layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.add([x, res2])
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(64, (4, 4), strides=(2, 2), padding='same')(x)
x = layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.add([x, res1])
x = layers.Conv2D(32, (3, 3), strides=(1, 1), padding='same')(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(3, (3, 3), strides=(1, 1), padding='same')(x)
x = layers.Activation('tanh')(x)
outputs = Lambda(lambda x: x)(x)
gen_model = tf.keras.Model(inputs = inputs, outputs=outputs)
return gen_model
def LSTM_Unet():
INPUT_SIZE = (496, 496, 1)
KERNEL = 3
NUM_CLASSES = 9
POOL_SIZE = (2, 2)
CONV_PARAMS = {
'activation': 'relu',
'padding': 'same',
'kernel_initializer': 'he_normal'
}
inputs = layers.Input(INPUT_SIZE)
#Downward slope
down_conv_1 = layers.Conv2D(32, KERNEL, **CONV_PARAMS)(inputs)
down_conv_1 = layers.Conv2D(32, KERNEL, **CONV_PARAMS)(down_conv_1)
down_pool_1 = layers.MaxPooling2D(POOL_SIZE)(down_conv_1)
down_conv_2 = layers.Conv2D(64, KERNEL, **CONV_PARAMS)(down_pool_1)
down_conv_2 = layers.Conv2D(64, KERNEL, **CONV_PARAMS)(down_conv_2)
down_pool_2 = layers.MaxPooling2D(POOL_SIZE)(down_conv_2)
down_conv_3 = layers.Conv2D(128, KERNEL, **CONV_PARAMS)(down_pool_2)
down_conv_3 = layers.Conv2D(128, KERNEL, **CONV_PARAMS)(down_conv_3)
down_pool_3 = layers.MaxPooling2D(POOL_SIZE)(down_conv_3)
down_conv_4 = layers.Conv2D(256, KERNEL, **CONV_PARAMS)(down_pool_3)
down_conv_4 = layers.Conv2D(256, KERNEL, **CONV_PARAMS)(down_conv_4)
down_pool_4 = layers.MaxPooling2D(POOL_SIZE)(down_conv_4)
#Re-net Center
down_pool_last = down_pool_4
bidirect = ReNetCell(down_pool_last)
center_drop = bidirect
#Upward slope
up_sampling_conv_4 = layers.Conv2D(256, 2, **CONV_PARAMS)(
layers.UpSampling2D(size=POOL_SIZE)(center_drop))
up_merge_4 = layers.Concatenate(axis=3)([up_sampling_conv_4, down_conv_4])
up_conv_4 = layers.Conv2D(256, KERNEL, **CONV_PARAMS)(up_merge_4)
up_conv_4 = layers.Conv2D(256, KERNEL, **CONV_PARAMS)(up_conv_4)
up_sampling_conv_3 = layers.Conv2D(128, 2, **CONV_PARAMS)(
layers.UpSampling2D(size=POOL_SIZE)(up_conv_4))
up_merge_3 = layers.Concatenate(axis=3)([up_sampling_conv_3, down_conv_3])
up_conv_3 = layers.Conv2D(128, KERNEL, **CONV_PARAMS)(up_merge_3)
up_conv_3 = layers.Conv2D(128, KERNEL, **CONV_PARAMS)(up_conv_3)
up_sampling_conv_2 = layers.Conv2D(64, 2, **CONV_PARAMS)(
layers.UpSampling2D(size=POOL_SIZE)(up_conv_3))
up_merge_2 = layers.Concatenate(axis=3)([up_sampling_conv_2, down_conv_2])
up_conv_2 = layers.Conv2D(64, KERNEL, **CONV_PARAMS)(up_merge_2)
up_conv_2 = layers.Conv2D(64, KERNEL, **CONV_PARAMS)(up_conv_2)
up_sampling_conv_1 = layers.Conv2D(32, 2, **CONV_PARAMS)(
layers.UpSampling2D(size=POOL_SIZE)(up_conv_2))
up_merge_1 = layers.Concatenate(axis=3)([up_sampling_conv_1, down_conv_1])
up_conv_1 = layers.Conv2D(32, KERNEL, **CONV_PARAMS)(up_merge_1)
up_conv_1 = layers.Conv2D(32, KERNEL, **CONV_PARAMS)(up_conv_1)
output = layers.Conv2D(
NUM_CLASSES,
1,
activation=None,
padding='same',
kernel_initializer='he_normal',
)(up_conv_1)
output = layers.Softmax(axis=-1)(output)
model = models.Model(inputs=inputs, outputs=output)
return model
def buildMyModelV4(self, shape, n_cls):
model = Sequential()
model.add(layers.Conv2D(256, (3, 3), padding='same',
input_shape=shape))
# kernel_regularizer=l2(0.01)))
# model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D())
model.add(layers.Dropout(0.25))
layer05 = layers.Dense(128, activation='sigmoid')(layers.Flatten()(
model.output))
model.add(layers.Conv2D(128, (3, 3), padding='same'))
# kernel_regularizer=l2(0.01)))
# model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D())
model.add(layers.Dropout(0.25))
layer04 = layers.Dense(128, activation='sigmoid')(layers.Flatten()(
model.output))
model.add(layers.Conv2D(128, (3, 3), padding='same'))
# kernel_regularizer=l2(0.01)))
# model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.Dropout(0.25))
model.add(layers.Conv2D(64, (3, 3), padding='same'))
# kernel_regularizer=l2(0.01)))
# model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.Dropout(0.25))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='sigmoid'))
# kernel_regularizer=l2(0.01)))
model.add(layers.Dropout(0.25))
layer03 = model.output
model.add(layers.Dense(256, activation='sigmoid'))
# kernel_regularizer=l2(0.01)))
model.add(layers.Dropout(0.25))
layer02 = model.output
model.add(layers.Dense(128, activation='sigmoid'))
# kernel_regularizer=l2(0.01)))
self.e_len = [128, 128, 512, 256, 128]
self.input = model.input
self.output = model.output
input_a = layers.Input(shape=shape)
input_p = layers.Input(shape=shape)
input_n = layers.Input(shape=shape)
layer05_model = Model(inputs=model.input, outputs=layer05)
layer05_a = layer05_model(input_a)
layer05_p = layer05_model(input_p)
layer05_n = layer05_model(input_n)
layer04_model = Model(inputs=model.input, outputs=layer04)
layer04_a = layer04_model(input_a)
layer04_p = layer04_model(input_p)
layer04_n = layer04_model(input_n)
layer03_model = Model(inputs=model.input, outputs=layer03)
layer03_a = layer03_model(input_a)
layer03_p = layer03_model(input_p)
layer03_n = layer03_model(input_n)
layer02_model = Model(inputs=model.input, outputs=layer02)
layer02_a = layer02_model(input_a)
layer02_p = layer02_model(input_p)
layer02_n = layer02_model(input_n)
embed_model = Model(inputs=model.input, outputs=model.output)
embed_a = embed_model(input_a)
embed_p = embed_model(input_p)
embed_n = embed_model(input_n)
concat = layers.Concatenate()([
layer05_a, layer05_p, layer05_n, layer04_a, layer04_p, layer04_n,
layer03_a, layer03_p, layer03_n, layer02_a, layer02_p, layer02_n,
embed_a, embed_p, embed_n
])
self.model = Model(inputs=[input_a, input_p, input_n], outputs=concat)
return self
test_labels) = util.cifar10_load_data(dirname)
class_names = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck'
]
model = models.Sequential()
model.add(
layers.Conv2D(filters=6,
kernel_size=(5, 5),
strides=1,
activation='relu',
input_shape=(32, 32, 3)))
model.add(layers.Dropout(rate=0.25))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=2))
model.add(
layers.Conv2D(filters=16, kernel_size=(5, 5), strides=1,
activation='relu'))
model.add(layers.Dropout(rate=0.25))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=2))
model.add(layers.Flatten())
model.add(layers.Dense(120, activation='relu'))
model.add(layers.Dense(84, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
def build_model(nx: Optional[int] = None,
ny: Optional[int] = None,
channels: int = 3,
layer_depth: int = 3,
filters_root: int = 64,
kernel_size: int = 3,
pool_size: int = 2,
dropout_rate: int = 0.5,
padding: str = "same",
activation: Union[str, Callable] = "relu",
norm_layer: str = 'instance') -> Model:
"""
Constructs a U-Net model
:param nx: (Optional) image size on x-axis
:param ny: (Optional) image size on y-axis
:param channels: number of channels of the input tensors
:param layer_depth: total depth of unet
:param filters_root: number of filters in top unet layer
:param kernel_size: size of convolutional layers
:param pool_size: size of maxplool layers
:param dropout_rate: rate of dropout
:param padding: padding to be used in convolutions
:param activation: activation to be used
:return: A TF Keras model
"""
inputs = Input(shape=(nx, ny, channels), name="inputs")
x = inputs
contracting_layers = {}
conv_params = dict(filters_root=filters_root,
kernel_size=kernel_size,
dropout_rate=dropout_rate,
padding=padding,
activation=activation,
norm_layer=norm_layer)
for layer_idx in range(0, layer_depth - 1):
x = ConvBlock(layer_idx, **conv_params)(x)
contracting_layers[layer_idx] = x
x = layers.MaxPooling2D((pool_size, pool_size))(x)
x = ConvBlock(layer_idx + 1, **conv_params)(x)
for layer_idx in range(layer_idx, -1, -1):
x = UpconvBlock(layer_idx, filters_root, kernel_size, pool_size,
padding, activation)(x)
x = CropConcatBlock()(x, contracting_layers[layer_idx])
x = ConvBlock(layer_idx, **conv_params)(x)
x = layers.Conv2D(filters=channels,
kernel_size=(1, 1),
kernel_initializer=_get_kernel_initializer(
filters_root, kernel_size),
strides=1,
padding=padding)(x)
outputs = layers.Activation("tanh", name="outputs")(x)
model = Model(inputs, outputs, name="unet")
return model
batch_size = 32
# create model
model = models.Sequential()
model.add(layers.InputLayer(input_shape=(28, 28, 1))) # input layer
model.add(
layers.Conv2D( # conv layer
filters=32,
strides=(1, 1),
kernel_size=[3, 3],
padding="same",
activation='relu',
kernel_initializer=tf.contrib.layers.xavier_initializer(
uniform=False)))
model.add(layers.BatchNormalization()) # batch norm
model.add(layers.MaxPooling2D((2, 2))) # max pooling
model.add(layers.Flatten()) # flatten
model.add(layers.Dense(784, activation='relu')) # fully-connected 784
model.add(layers.Dense(10)) # fully-connected 10
model.add(layers.Softmax()) # softmax output
# compile model w/ Adam optimizer + cross entropy loss
model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# ## 2.2 Model Training {-}
# callback to test after each epoch
class TestCallback(tf.keras.callbacks.Callback):
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
# normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
# image_batch, labels_batch = next(iter(normalized_ds))
# first_image = image_batch[0]
# # Notice the pixels values are now in `[0,1]`.
# print(np.min(first_image), np.max(first_image))
num_classes = 10
model = Sequential([
layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
layers.Conv2D(16, 3, padding='same', activation='relu'),
layers.MaxPooling2D(pool_size=(2,4)),
layers.Conv2D(32, 3, padding='same', activation='relu'),
layers.MaxPooling2D(pool_size=(2,4)),
layers.Conv2D(64, 3, padding='same', activation='relu'),
layers.MaxPooling2D(pool_size=(2,4)),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(num_classes)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
def build_train(self):
AUTOTUNE = self.AUTOTUNE
spectrogram_ds = self.spectrogram_ds
commands = self.commands
# repeat the training set preprocessing on the validation and test sets.
train_ds = spectrogram_ds
val_ds = self.preprocess_dataset(self.val_files)
test_ds = self.preprocess_dataset(self.test_files)
# Batch the training and validation sets for model training.
batch_size = 64
train_ds = train_ds.batch(batch_size)
val_ds = val_ds.batch(batch_size)
# Add dataset cache() and prefetch() operations to reduce read latency while training the model.
train_ds = train_ds.cache().prefetch(AUTOTUNE)
val_ds = val_ds.cache().prefetch(AUTOTUNE)
self.test_ds = test_ds
if not os.path.exists("speech.model"):
for spectrogram, _ in spectrogram_ds.take(1):
input_shape = spectrogram.shape
print('Input shape:', input_shape)
num_labels = len(commands)
norm_layer = preprocessing.Normalization()
norm_layer.adapt(spectrogram_ds.map(lambda x, _: x))
model = models.Sequential([
layers.Input(shape=input_shape),
preprocessing.Resizing(32, 32),
norm_layer,
layers.Conv2D(32, 3, activation='relu'),
layers.Conv2D(64, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Dropout(0.25),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dropout(0.5),
layers.Dense(num_labels),
])
model.summary()
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True),
metrics=['accuracy'],
)
EPOCHS = 10
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=EPOCHS,
callbacks=tf.keras.callbacks.EarlyStopping(verbose=1,
patience=2),
)
metrics = history.history
fig4 = plt.figure()
plt.plot(history.epoch, metrics['loss'], metrics['val_loss'])
plt.legend(['loss', 'val_loss'])
model.save('speech.model')
faces = normalizeFacesData(FER_DATA)
faces = np.asarray(faces)
emotions = FER_DATA['emotion'].to_numpy()
emotions = np.expand_dims(emotions, -1)
#-------------------------------------------------------------
#KERAS MODEL
#-------------------------------------------------------------
model = keras.Sequential()
model.add(
layers.Conv2D(32, (3, 3),
padding='same',
activation='relu',
input_shape=(48, 48, 1)))
model.add(layers.Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(64, (3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(128, (3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Flatten()
) # this converts our 3D feature maps to 1D feature vectors
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(64, activation='relu'))
def Trainingresult_1(): #Q5.4
global class_name
#與mnist.load_data()不同, cifar10.load_data() 會固定去 Cifar-10 網頁下載資料集,
(x_train_image,
y_train_label), (x_test_image,
y_test_label) = datasets.cifar10.load_data()
class_name = [
'airplane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck'
]
#pretreatment
x_train_normalize = x_train_image.astype('float32') / 255.0
x_test_normalize = x_test_image.astype('float32') / 255.0
#setting
learning_rate_set = 0.001
batch_size_set = 100
epochs_set = 50
#model setting
model = models.Sequential()
model.add(
layers.Conv2D(filters=6,
kernel_size=(5, 5),
input_shape=(32, 32, 3),
activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(filters=16, kernel_size=(5, 5), activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(120, activation='relu'))
model.add(layers.Dense(84, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
# model.compile(loss='sparse_categorical_crossentropy',optimizer = tf.optimizers.SGD(lr = learning_rate_set, clipnorm = 1), metrics=['accuracy']) #測出來很怪
model.compile(
optimizer=tf.optimizers.Adam(learning_rate=learning_rate_set),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(x=x_train_normalize,
y=y_train_label,
validation_data=(x_test_normalize, y_test_label),
epochs=epochs_set,
batch_size=batch_size_set)
# 將模型儲存至 HDF5 檔案中
model.save("my_model.h5")
plt.subplot(2, 1, 1)
plt.plot(history.history['accuracy'], '#3A5FCD', label='accuracy')
plt.xlabel('epoch')
plt.ylabel('Accuracy')
plt.legend(loc='upper right')
plt.subplot(2, 1, 2)
plt.plot(history.history['loss'], '#3A5FCD', label='loss')
plt.xlabel('epoch')
plt.ylabel('Loss')
plt.legend(loc='upper right')
plt.savefig('5/5_4.png')
plt.show()
import tensorflow as tf
from tensorflow.python.ops import summary_ops_v2
from tensorflow import keras
from tensorflow.keras import datasets, layers, models, optimizers, metrics
model = tf.keras.Sequential([
layers.Reshape(
target_shape=[28, 28, 1],
input_shape=(28, 28,)),
layers.Conv2D(2, 5, padding='same', activation=tf.nn.relu),
layers.MaxPooling2D((2, 2), (2, 2), padding='same'),
layers.Conv2D(4, 5, padding='same', activation=tf.nn.relu),
layers.MaxPooling2D((2, 2), (2, 2), padding='same'),
layers.Flatten(),
layers.Dense(32, activation=tf.nn.relu),
layers.Dropout(rate=0.4),
layers.Dense(10)])
compute_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
compute_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
optimizer = optimizers.SGD(learning_rate=0.01, momentum=0.5)
def mnist_datasets():
(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()
# Numpy defaults to dtype=float64; TF defaults to float32. Stick with float32.
def ResNet50_mod(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
"""Instantiates the ResNet50 architecture.
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`.
# Arguments
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)` (with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional block.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional block, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
# global backend, layers, models, keras_utils
# backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
# if not (weights in {'imagenet', None} or os.path.exists(weights)):
# raise ValueError('The `weights` argument should be either '
# '`None` (random initialization), `imagenet` '
# '(pre-training on ImageNet), '
# 'or the path to the weights file to be loaded.')
# if weights == 'imagenet' and include_top and classes != 1000:
# raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
# ' as true, `classes` should be 1000')
# # Determine proper input shape
# input_shape = _obtain_input_shape(input_shape,
# default_size=224,
# min_size=32,
# data_format=backend.image_data_format(),
# require_flatten=include_top,
# weights=weights)
# if input_tensor is None:
# img_input = layers.Input(shape=input_shape)
# else:
# if not backend.is_keras_tensor(input_tensor):
# img_input = layers.Input(tensor=input_tensor, shape=input_shape)
# else:
# img_input = input_tensor
# if backend.image_data_format() == 'channels_last':
# bn_axis = 3
# else:
# bn_axis = 1
bn_axis = -1
img_input = Input(shape=input_shape)
x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input)
x = layers.Conv2D(64, (7, 7),
strides=(2, 2),
padding='valid',
kernel_initializer='he_normal',
name='conv1')(x)
x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = layers.Activation('relu')(x)
x = layers.ZeroPadding2D(padding=(1, 1), name='pool1_pad')(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = layers.Dense(classes, activation='softmax', name='fc1000')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D()(x)
else:
warnings.warn('The output shape of `ResNet50(include_top=False)` '
'has been changed since Keras 2.2.0.')
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = keras_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = models.Model(inputs, x, name='resnet50')
# Load weights.
if weights == 'imagenet':
if include_top:
weights_path = data_utils.get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels.h5',
WEIGHTS_PATH,
cache_subdir='models',
md5_hash='a7b3fe01876f51b976af0dea6bc144eb')
else:
weights_path = data_utils.get_file(
'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a268eb855778b3df3c7506639542a6af')
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
model = models.Sequential()
model.add(
layers.experimental.preprocessing.Rescaling(1. / 255,
input_shape=(IMG_WIDTH,
IMG_HEIGHT,
IMG_DEPTH)))
model.add(layers.experimental.preprocessing.RandomRotation(10 / 360)),
model.add(
layers.Conv2D(filters=32,
kernel_size=(3, 3),
padding="same",
activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2), strides=2, padding="valid"))
model.add(
layers.Conv2D(filters=64,
kernel_size=(3, 3),
padding="same",
activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2), strides=2, padding="valid"))
model.add(
layers.Conv2D(filters=128,
kernel_size=(3, 3),
padding="same",
activation='relu'))
model.add(layers.BatchNormalization())
model.add(layers.MaxPooling2D((2, 2), strides=2, padding="valid"))