def k_autoencoder4(latent_dim, folder):
inputs = kst.md_input((32, 32, 3))
net = kst.conv2d(inputs, 64, (3, 3)) # 32,32,64
net = kst.activation(net, "relu")
net = kst.conv2d(net, 64, (3, 3)) # 32,32,64
net = kst.activation(net, "relu")
net = kst.maxpool2d(net) # 16,16,64
net = kst.conv2d(net, 128, (3, 3)) # 16,16,128
net = kst.activation(net, "relu")
net = kst.conv2d(net, 128, (3, 3)) # 16,16,128
net = kst.activation(net, "relu")
net = kst.maxpool2d(net) # 8,8,128
net = kst.conv2d(net, 256, (3, 3)) # 8,8,256
net = kst.activation(net, "relu")
net = kst.conv2d(net, 256, (3, 3))
net = kst.flatten(net) # 8*8*256
net = kst.dense(net, 2048) # 1024
net = kst.activation(net, "relu")
net = kst.dense(net, latent_dim)
encode = kst.activation(net, "relu")
net = kst.dense(encode, 2048) #19
net = kst.activation(net, "relu")
net = kst.dense(net, 8 * 8 * 256)
net = kst.activation(net, "relu")
net = kst.reshape(net, (8, 8, 256)) # 4,4,512
# net = kst.deconv2d(net, 512, (3, 3))
# net = kst.activation(net, "relu")
# net = kst.deconv2d(net, 512, (3, 3))
# net = kst.upsampling(net, 2) # 8,8,512
net = kst.deconv2d(net, 256, (3, 3)) # 8,8,256
net = kst.activation(net, "relu")
net = kst.deconv2d(net, 256, (3, 3))
net = kst.upsampling(net, 2) # 16,16,256
net = kst.deconv2d(net, 128, (3, 3)) # 16,16,128
net = kst.activation(net, "relu")
net = kst.deconv2d(net, 128, (3, 3))
net = kst.activation(net, "relu")
net = kst.upsampling(net, 2) # 32,32,128
net = kst.deconv2d(net, 64, (3, 3)) # 32,32,64
net = kst.activation(net, "relu")
net = kst.deconv2d(net, 64, (3, 3))
net = kst.activation(net, "relu")
net = kst.deconv2d(net, 3, (3, 3)) # 32,32,3
decode = kst.activation(net, "sigmoid")
auto_encoder = Model(inputs, decode)
net_struct = kst.model_summary(auto_encoder,
print_out=True,
save_dir=folder + "/model_summary.txt")
encoder = Model(inputs, encode)
encoded_input = kst.md_input(shape=(latent_dim, ))
decoding = auto_encoder.layers[19](encoded_input)
for layer in auto_encoder.layers[20:]:
decoding = layer(decoding)
decoder = Model(encoded_input, decoding)
return auto_encoder, encoder, decoder
model.add(LeakyReLU(af[1]))
model.add(Flatten())
for dnl in denl:
model.add(Dense(units=dnl, kernel_initializer=wi[2], bias_initializer=bi[2]))
model.add(BatchNormalization())
if af[0] == 'relu':
model.add(Activation('relu'))
elif af[0] == 'leaky_relu':
model.add(LeakyReLU(af[1]))
model.add(Dropout(dp))
model.add(Dense(units=7, kernel_initializer=wi[2]))
model.add(Activation('softmax'))
net_rpt = knt.model_summary(model, param)
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=lr), metrics=['accuracy'])
mc = ModelCheckpoint(os.path.join(dir, 'best_model_fold' + str(nfid+1) + '.h5'), monitor='val_acc', mode='max', verbose=1, save_best_only=True)
# hist = model.fit(x_train, y_train,
# batch_size=bs, epochs=epochs,
# validation_data=(x_valid, y_valid), callbacks=[mc])
hist = model.fit_generator(
train_generator,
steps_per_epoch=train_generator.n // bs,
epochs=epochs,
validation_data=validate_generator,
validation_steps=validate_generator.n // bs,
callbacks=[mc]
)
acc_train = hist.history['acc']
net = kst.maxpool2d(net)
net = kst.flatten(net)
net = kst.dense(net, 1024)
net = kst.batch_norm(net)
net = kst.activation(net, "LeakyReLU")
net = kst.dropout(net, 0.5)
net = kst.dense(net, 512)
net = kst.batch_norm(net)
net = kst.activation(net, "LeakyReLU")
net = kst.dropout(net, 0.5)
net = kst.dense(net, 7)
net_out = kst.activation(net, "softmax")
model = Model(inputs, net_out)
net_rpt = kst.model_summary(model, param_dict=param)
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=learn_rate), metrics=['accuracy'])
# create project
tn = time.localtime()
project = "./logs/hw3/D{0:4d}{1:02d}{2:02d}T{3:02d}{4:02d}".format(tn[0], tn[1], tn[2], tn[3], tn[4])
os.mkdir(project)
mc = ModelCheckpoint(os.path.join(project, 'best_model.h5'), monitor='val_acc', mode='max', verbose=1, save_best_only=True)
hist = model.fit_generator(
train_generator,
steps_per_epoch=train_generator.n // bt_size,
epochs=epochs,
validation_data=validate_generator,
def k_inception_v3(n_class, dp):
# TODO: make sure the reliability"
inputs = kst.md_input((299, 299, 1))
# inputs = K.resize_images(inputs, 299, 299, data_format="channels_last")
net = kst.conv2d(inputs, 32, (3, 3), stride=2, padding="VALID")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.conv2d(net, 32, (3, 3), padding="VALID")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.conv2d(net, 64, (3, 3), padding="SAME")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.maxpool2d(net, kernel_size=(3, 3), stride=2, padding="VALID")
net = kst.conv2d(net, 80, (1, 1), padding="VALID")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.conv2d(net, 192, (3, 3), padding="VALID")
net = kst.batch_norm(net)
net = kst.activation(net, "relu")
net = kst.maxpool2d(net, (3, 3), padding="VALID")
# mixed 5b:
branch0 = kst.conv2d(net, 64, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 48, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 64, (5, 5))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 64, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 32, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 5c:
branch0 = kst.conv2d(net, 64, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 48, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 64, (5, 5))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 64, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 64, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 5d:
branch0 = kst.conv2d(net, 64, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 48, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 64, (5, 5))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 64, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 96, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 64, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 6a:
branch0 = kst.conv2d(net, 348, (3, 3), stride=2, padding="VALID")
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 64, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 96, (3, 3))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 96, (3, 3), stride=2, padding="VALID")
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.maxpool2d(net, (3, 3), padding="VALID")
net = kst.concat([branch0, branch1, branch2], axis=3)
# mixed 6b:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 128, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 128, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 128, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 128, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 128, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 128, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 6c:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 160, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 160, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 160, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 6d:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 160, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 160, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 160, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 160, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 6e:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 192, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.conv2d(net, 192, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (7, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch2 = kst.conv2d(branch2, 192, (1, 7))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, 'relu')
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
aux_logits = net
# mixed 7a:
branch0 = kst.conv2d(net, 192, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch0 = kst.conv2d(branch0, 320, (3, 3), stride=2, padding="VALID")
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 192, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (1, 7))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (7, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1 = kst.conv2d(branch1, 192, (3, 3), stride=2, padding="VALID")
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch2 = kst.maxpool2d(net, (3, 3), padding="VALID")
net = kst.concat([branch0, branch1, branch2], axis=3)
# mixed 7b:
branch0 = kst.conv2d(net, 320, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 384, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1a = kst.conv2d(branch1, 384, (1, 3))
branch1a = kst.batch_norm(branch1a)
branch1a = kst.activation(branch1a, "relu")
branch1b = kst.conv2d(branch1, 384, (3, 1))
branch1b = kst.batch_norm(branch1b)
branch1b = kst.activation(branch1b, "relu")
branch1 = kst.concat([branch1a, branch1b], axis=3)
branch2 = kst.conv2d(net, 448, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, "relu")
branch2 = kst.conv2d(branch2, 384, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, "relu")
branch2a = kst.conv2d(branch2, 384, (1, 3))
branch2a = kst.batch_norm(branch2a)
branch2a = kst.activation(branch2a, "relu")
branch2b = kst.conv2d(branch1, 384, (3, 1))
branch2b = kst.batch_norm(branch2b)
branch2b = kst.activation(branch2b, "relu")
branch2 = kst.concat([branch2a, branch2b], axis=3)
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# mixed 7c:
branch0 = kst.conv2d(net, 320, (1, 1))
branch0 = kst.batch_norm(branch0)
branch0 = kst.activation(branch0, "relu")
branch1 = kst.conv2d(net, 384, (1, 1))
branch1 = kst.batch_norm(branch1)
branch1 = kst.activation(branch1, "relu")
branch1a = kst.conv2d(branch1, 384, (1, 3))
branch1a = kst.batch_norm(branch1a)
branch1a = kst.activation(branch1a, "relu")
branch1b = kst.conv2d(branch1, 384, (3, 1))
branch1b = kst.batch_norm(branch1b)
branch1b = kst.activation(branch1b, "relu")
branch1 = kst.concat([branch1a, branch1b], axis=3)
branch2 = kst.conv2d(net, 448, (1, 1))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, "relu")
branch2 = kst.conv2d(branch2, 384, (3, 3))
branch2 = kst.batch_norm(branch2)
branch2 = kst.activation(branch2, "relu")
branch2a = kst.conv2d(branch2, 384, (1, 3))
branch2a = kst.batch_norm(branch2a)
branch2a = kst.activation(branch2a, "relu")
branch2b = kst.conv2d(branch1, 384, (3, 1))
branch2b = kst.batch_norm(branch2b)
branch2b = kst.activation(branch2b, "relu")
branch2 = kst.concat([branch2a, branch2b], axis=3)
branch3 = kst.avgpool2d(net, (3, 3))
branch3 = kst.conv2d(branch3, 192, (1, 1))
branch3 = kst.batch_norm(branch3)
branch3 = kst.activation(branch3, "relu")
net = kst.concat([branch0, branch1, branch2, branch3], axis=3)
# auxlogits:
aux_logits = kst.avgpool2d(aux_logits, (5, 5), stride=3, padding="VALID")
aux_logits = kst.conv2d(aux_logits, 128, (1, 1))
aux_logits = kst.batch_norm(aux_logits)
aux_logits = kst.activation(aux_logits, "relu")
aux_logits = kst.conv2d(aux_logits, 768, (5, 5), padding="VALID")
aux_logits = kst.batch_norm(aux_logits)
aux_logits = kst.activation(aux_logits, "relu")
aux_logits = kst.conv2d(aux_logits, n_class, (1, 1))
aux_logits = kst.flatten(aux_logits)
# aux_logits = kst.squeeze(aux_logits, 1)
# aux_logits = kst.squeeze(aux_logits, 1)
aux_predict = kst.activation(aux_logits, "softmax")
# Logits:
net = kst.avgpool2d(net, (8, 8), padding="VALID")
net = kst.dropout(net, dp)
logits = kst.conv2d(net, n_class, (1, 1))
# logits = kst.squeeze(logits, 1)
# logits = kst.squeeze(logits, 1)
logits = kst.flatten(logits)
predict = kst.activation(logits, "softmax")
inception_v3 = Model(inputs, [predict, aux_predict])
net_struct = kst.model_summary(inception_v3, print_out=True)
# aux_inception = Model(inputs, aux_predict)
return inception_v3