include_top=False,
input_shape=(ROWS, COLS,
CHANNELS))
Xception_model = keras.applications.Xception(weights='imagenet',
include_top=False,
input_shape=(ROWS, COLS,
CHANNELS))
InceptionV3_layers = InceptionV3_model(Inp)
InceptionV3_layers = keras.layers.GlobalAveragePooling2D()(InceptionV3_layers)
Xception_layers = Xception_model(Inp)
Xception_layers = keras.layers.GlobalAveragePooling2D()(Xception_layers)
x = keras.layers.Concatenate()([InceptionV3_layers, Xception_layers])
output = keras.layers.Dense(2, activation='softmax')(x)
model = keras.Model(inputs=Inp, outputs=output)
for layer in InceptionV3_model.layers:
layer.trainable = False
for layer in Xception_model.layers:
layer.trainable = False
keras.utils.plot_model(model,
show_shapes=True,
show_layer_names=True,
to_file='MultiNetV1_model.pdf')
train_datagen = keras.preprocessing.image.ImageDataGenerator(
rotation_range=40, # 随机旋转度数
width_shift_range=0.2, # 随机水平平移
height_shift_range=0.2, # 随机竖直平移
rescale=1 / 255, # 数据归一化
FlattenLayer = keras.layers.Flatten()
#
path = FlattenLayer(inputt)
for i in range(0,5):
LayerDense1 = keras.layers.Dense(10, activation=None, use_bias=True, kernel_initializer='glorot_uniform')
path = LayerDense1(path)
LayerPReLU1 = keras.layers.PReLU(alpha_initializer='zeros', shared_axes=None)
path = LayerPReLU1(path)
#
LayerDenseN = keras.layers.Dense(1,activation=None, use_bias=True, kernel_initializer='glorot_uniform')
output = LayerDenseN(path)
##---------------------------------
## Creation of TensorFlow Model
##---------------------------------
covidModel = keras.Model(inputt, output, name='covidEstimatior')
#
covidModel.summary() # Display summary
#
##Włączenia procesu uczenia
#
rmsOptimizer = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
#
covidModel.compile(optimizer=rmsOptimizer,loss=keras.losses.mean_absolute_error)
#covidModel.compile(optimizer=rmsOptimizer,loss=keras.losses.binary_crossentropy,metrics=['accuracy'])
#
covidModel.fit(BazaVec, BazaAns, epochs=125, batch_size=10, shuffle=True)
covidModel.save('covid.h5')
##Przetestować / użyć sieci
def __init__(self, w, h, to_win, num_playouts=100, C=.4, model_save=None):
self.game_w = w
self.game_h = h
self.game_tw = to_win
if model_save:
print("loading model")
#self.model = tf.saved_model.load(model_save)
self.model = keras.models.load_model(model_save)
else:
inpt = keras.Input(shape=(w, h, 4))
# first convolution
# 5x5 convolution filter with zero-padded board
conv = keras.layers.Conv2D(32,
5,
data_format="channels_last",
padding="same")
bn = keras.layers.BatchNormalization()
relu = keras.layers.ReLU()
x = relu(bn(conv(inpt)))
# residual layers
for _ in range(1):
res = x
conv = keras.layers.Conv2D(32,
3,
data_format="channels_last",
padding="same")
bn = keras.layers.BatchNormalization()
relu = keras.layers.ReLU()
x = relu(bn(conv(x)))
conv = keras.layers.Conv2D(32,
3,
data_format="channels_last",
padding="same")
bn = keras.layers.BatchNormalization()
x = bn(conv(x))
x = keras.layers.Add()([x, res])
relu = keras.layers.ReLU()
x = relu(x)
# main neural network body
self.body = keras.Model(inputs=inpt, outputs=x)
# value head
conv = keras.layers.Conv2D(1, 1, data_format="channels_last")
bn = keras.layers.BatchNormalization()
relu = keras.layers.ReLU()
v = relu(bn(conv(x)))
flat = keras.layers.Flatten()
dens = keras.layers.Dense(128)
relu = keras.layers.ReLU()
v = relu(dens(flat(v)))
dens = keras.layers.Dense(1, activation=keras.activations.tanh)
v = dens(v)
# policy head
conv = keras.layers.Conv2D(2, 1, data_format="channels_last")
bn = keras.layers.BatchNormalization()
relu = keras.layers.ReLU()
p = relu(bn(conv(x)))
flat = keras.layers.Flatten()
dens = keras.layers.Dense(w * h)
p = dens(flat(p))
self.model = keras.Model(inputs=inpt,
outputs=[v, p],
name="value network")
self.model.compile(optimizer=keras.optimizers.SGD(lr=0.01,
decay=1e-6),
loss="mean_squared_error")
self.optimizer = keras.optimizers.SGD(.05)
self.num_playouts = num_playouts
self.C = C
FlattenLayer = keras.layers.Flatten()
path = FlattenLayer(input)
for i in range(0,6):
LayerDense1 = keras.layers.Dense(50, activation=None, use_bias=True, kernel_initializer='glorot_uniform')
path = LayerDense1(path)
LayerPReLU1 = keras.layers.PReLU(alpha_initializer='zeros', shared_axes=None)
path = LayerPReLU1(path)
LayerDenseN = keras.layers.Dense(1, activation=keras.activations.sigmoid, use_bias=True, kernel_initializer='glorot_uniform')
output = LayerDenseN(path)
#---------------------------------
# Creation of TensorFlow Model
#---------------------------------
genderModel = keras.Model(input, output, name='genderEstimatior')
genderModel.summary() # Display summary
#Włączenia procesu uczenia
rmsOptimizer = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
genderModel.compile(optimizer=rmsOptimizer,loss=keras.losses.binary_crossentropy,metrics=['accuracy'])
genderModel.fit(BazaImg, BazaAns, epochs=100, batch_size=10, shuffle=True)
genderModel.save('siec.h5')
path = FlattenLayer(input)
for i in range(0,6):
LayerDense1 = keras.layers.Dense(20, activation=None, use_bias=True, kernel_initializer='glorot_uniform')
path = LayerDense1(path)
LayerPReLU1 = keras.layers.PReLU(alpha_initializer='zeros', shared_axes=None)
path = LayerPReLU1(path)
LayerDenseN = keras.layers.Dense(1, activation=None, use_bias=True, kernel_initializer='glorot_uniform')
output = LayerDenseN(path)
#---------------------------------
# Creation of TensorFlow Model
#---------------------------------
new_casesModel = keras.Model(input, output, name='COVID_Estimatior')
new_casesModel.summary() # Display summary
#Włączenia procesu uczenia
rmsOptimizer = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
new_casesModel.compile(optimizer=rmsOptimizer,loss=keras.losses.mean_absolute_error)
new_casesModel.fit(Baza, BazaAns, epochs=150, batch_size=10, shuffle=True)
new_casesModel.save('virus.h5')
#Przetestować / użyć sieci
covid = new_casesModel.predict(BazaPred)
print((covid[0]+1)*MaxAns)
