y = np.array(labels)
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(y)
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
df_y = onehot_encoder.fit_transform(integer_encoded)
df_x = np.array(df_x)
df_y = np.array(df_y)
x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size=0.25, random_state=4)
#***TO DO: add more layers (conv2d, maxpooling and dense, dropout), test different activations, test different optimizers, change stride length
#Create 2D CNN model architecture (use channels for depth)
model = Sequential()
model.add(Conv2D(filters=8, kernel_size=(8, 8), activation='relu', strides=(1,1), input_shape=(155, 240, 240), padding="same"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=16, kernel_size=(8, 8), activation='relu', strides=(1,1)))
model.add(Flatten())
model.add(Dense(100))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])
model.summary()
model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=5, verbose=1)
model.save('mri_model.h5')
def create_model(horizon=1,
nb_train_samples=512,
batch_size=32,
feature_count=11):
x = Input(shape=(6, feature_count), name="input_layer")
conv = Conv1D(kernel_size=3, filters=5, activation='relu',
dilation_rate=1)(x)
conv2 = Conv1D(5,
kernel_size=3,
padding='causal',
strides=1,
activation='relu',
dilation_rate=2)(conv)
conv3 = Conv1D(5,
kernel_size=3,
padding='causal',
strides=1,
activation='relu',
dilation_rate=4)(conv2)
mp = MaxPooling1D(pool_size=2)(conv3)
# conv2 = Conv1D(filters=5, kernel_size=3, activation='relu')(mp)
# mp = MaxPooling1D(pool_size=2)(conv2)
lstm1 = GRU(16, return_sequences=True)(mp)
lstm2 = GRU(32, return_sequences=True)(lstm1)
shared_dense = Dense(64, name="shared_layer")(lstm2)
shared_dense = Flatten()(shared_dense)
sub1 = Dense(16, name="task1")(shared_dense)
# sub2 = Dense(16, name="task2")(shared_dense)
# sub3 = Dense(16, name="task3")(shared_dense)
# sub1 = GRU(units=16, name="task1")(shared_dense)
# sub2 = GRU(units=16, name="task2")(shared_dense)
# sub3 = GRU(units=16, name="task3")(shared_dense)
# out1_gp = Dense(1, name="out1_gp")(sub1)
out1 = Dense(1, name="out1")(sub1)
# out2 = Dense(1, name="out2")(sub2)
# out3 = Dense(1, name="out3")(sub3)
# Gaussian setting
gp_hypers = {'lik': -2.0, 'cov': [[-0.7], [0.0]]}
gp_params = {
'cov': 'SEiso',
'hyp_lik': -2.0,
'hyp_cov': [[-0.7], [0.0]],
'opt': {
'cg_maxit': 500,
'cg_tol': 1e-4
},
'grid_kwargs': {
'eq': 1,
'k': 1e2
},
'update_grid': True,
}
gp1 = GP(gp_hypers,
batch_size=batch_size,
nb_train_samples=nb_train_samples)
# gp2 = GP(gp_hypers, batch_size=batch_size, nb_train_samples=nb_train_samples)
# gp3 = GP(gp_hypers, batch_size=batch_size, nb_train_samples=nb_train_samples)
outputs = [gp1(out1)]
model = Model(inputs=x, outputs=outputs)
model.compile(optimizer='adam', loss='mse', metrics=['mae', 'mape', 'mse'])
# Callbacks
# callbacks = [EarlyStopping(monitor='val_mse', patience=10)]
model.summary()
return model
def create_model_mtl_mtv_exchange_rate(horizon=1,
nb_train_samples=512,
batch_size=32,
feature_count=6,
lag_time=6,
aux_feature_count=8):
x = Input(shape=(lag_time, feature_count), name="input_layer")
conv = Conv1D(filters=5, kernel_size=1, activation='relu')(x)
# conv = Conv1D(filters=5, kernel_size=3, padding='causal', strides=1, activation='relu', dilation_rate=2)(x)
conv = Conv1D(filters=5,
kernel_size=3,
padding='causal',
strides=1,
activation='relu',
dilation_rate=4)(conv)
mp = MaxPooling1D(pool_size=1)(conv)
# conv2 = Conv1D(filters=5, kernel_size=3, activation='relu')(mp)
# mp = MaxPooling1D(pool_size=2)(conv2)
lstm1 = GRU(8, return_sequences=True)(mp)
lstm2 = GRU(16, return_sequences=True)(lstm1)
# lstm2 = GRU(16, return_sequences=True)(mp)
shared_dense = Dense(32, name="shared_layer")(lstm2)
## sub1 is main task; units = reshape dimension multiplication
sub1 = GRU(units=(lag_time * aux_feature_count),
name="task1")(shared_dense)
sub2 = GRU(units=16, name="task2")(shared_dense)
sub3 = GRU(units=16, name="task3")(shared_dense)
sub4 = GRU(units=16, name="task4")(shared_dense)
sub5 = GRU(units=16, name="task5")(shared_dense)
sub1 = Reshape((lag_time, aux_feature_count))(sub1)
auxiliary_input = Input(shape=(lag_time, aux_feature_count),
name='aux_input')
concate = Concatenate(axis=-1)([sub1, auxiliary_input])
# out1_gp = Dense(1, name="out1_gp")(sub1)
out1 = Dense(8, name="spec_out1")(concate)
out1 = Flatten()(out1)
out1 = Dense(1, name="out1")(out1)
out2 = Dense(8, name="spec_out2")(sub2)
out2 = Dense(1, name="out2")(out2)
out3 = Dense(1, name="spec_out3")(sub3)
out3 = Dense(1, name="out3")(out3)
out4 = Dense(1, name="spec_out4")(sub4)
out4 = Dense(1, name="out4")(out4)
out5 = Dense(1, name="spec_out5")(sub5)
out5 = Dense(1, name="out5")(out5)
outputs = [out1, out2, out3, out4, out5]
# outputs = [out1, out2, out3, out4]
model = KerasModel(inputs=[x, auxiliary_input], outputs=outputs)
model.compile(optimizer='sgd',
loss='mse',
metrics=['mae', 'mape', 'mse'],
loss_weights=[1, 0.00, 0.00, 0.00, 0.00])
# Callbacks
# callbacks = [EarlyStopping(monitor='val_mse', patience=10)]
model.summary()
return model
horizontal_flip=True,
fill_mode="nearest")
valAug = ImageDataGenerator()
mean = np.array([123.68, 116.779, 103.939], dtype="float32")
trainAug.mean = mean
valAug.mean = mean
baseModel = ResNet50(weights="imagenet",
include_top=False,
input_tensor=Input(shape=(224, 224, 3)))
headModel = baseModel.output
headModel = AveragePooling2D(pool_size=(7, 7))(headModel)
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(512, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(len(lb.classes_), activation="softmax")(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)
for layer in baseModel.layers:
layer.trainable = False
print("[INFO] compiling model...")
opt = SGD(lr=1e-4, momentum=0.9, decay=1e-4 / args["epochs"])
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
print('Discriminator Model')
d_input_img = Input(shape=X_train.shape[1:])
"""
d_input_label2 = Dense(28*28,activation='relu')(d_input_label)
d_input_label2 = Reshape(X_train.shape[1:])(d_input_label2)
d_input = concatenate([d_input_img, d_input_label2])
"""
discriminator_ = Conv2D(64, (5, 5), padding='same',
strides=(2, 2))(d_input_img) #initialization
discriminator_ = LeakyReLU(0.2)(discriminator_)
discriminator_ = Conv2D(128, (5, 5), padding='same',
strides=(2, 2))(discriminator_)
discriminator_ = LeakyReLU(0.2)(discriminator_)
discriminator_ = Flatten()(discriminator_)
d_input_label = Input(shape=[10])
discriminator_ = concatenate([discriminator_, d_input_label])
discriminator_ = Dense(1)(discriminator_)
discriminator_ = Activation('sigmoid')(discriminator_)
discriminator = Model(inputs=[d_input_img, d_input_label],
outputs=discriminator_)
discriminator.compile(loss='binary_crossentropy',
optimizer=adam,
metrics=['accuracy'])
discriminator.trainable = False
print('CGAN model')
print('Y_train shape:', Y_train.shape)
X_train, Y_train = shuffle(X_train, Y_train)
image_input = Input(shape=(224, 224, 3))
model = VGG16(include_top=False,
weights='imagenet',
input_tensor=image_input,
pooling=None,
classes=nb_classes)
for layer in model.layers[:9]:
layer.trainable = False
output = model.output
output = Flatten()(output)
output = Dense(1024, activation="relu")(output)
predictions = Dense(10, activation="softmax")(output)
model_final = Model(input=model.input, output=predictions)
#keras.utils.multi_gpu_model(model, gpus=2, cpu_merge=False, cpu_relocation=False)
#opt = keras.optimizers.Adamax(lr=0.001, beta_1=0.9, beta_2=0.99, decay=1e-6)# best one
opt = optimizers.SGD(lr=0.0001, momentum=0.9)
model_final.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
def train():
model_final.fit(X_train, Y_train, batch_size=32, epochs=20, shuffle=True)
# Two more convolutional layers with kernel size (3,3) and filter 64
x = Conv2D(filters=64,
kernel_size=(3, 3),
padding='valid',
activation='elu',
kernel_regularizer=l2(0.001))(x)
x = Conv2D(filters=64,
kernel_size=(3, 3),
padding='valid',
activation='elu',
kernel_regularizer=l2(0.001))(x)
x = Dropout(0.5)(x)
# Add a flatten layer
x = Flatten()(x)
# -- Start of the comma.ai model
# three convolutional layers (note that this branch also starts with the normalised image)
y = Conv2D(filters=16,
kernel_size=(8, 8),
strides=(4, 4),
padding='same',
activation='elu')(nimg)
y = Conv2D(filters=32,
kernel_size=(5, 5),
strides=(2, 2),
padding='same',
activation='elu')(y)
y = Conv2D(filters=64,
kernel_size=(5, 5),
def custom_model_hand():
'''
USER CODE STARTS HERE
'''
image_model = Sequential()
image_model.add(ZeroPadding2D((2, 2), input_shape=(50, 50, 1)))
#54x54 fed in due to zero padding
image_model.add(Conv2D(8, (5, 5), activation='relu', name='conv1_1'))
image_model.add(ZeroPadding2D((2, 2)))
image_model.add(Conv2D(8, (5, 5), activation='relu', name='conv1_2'))
image_model.add(MaxPooling2D((2, 2),
strides=(2, 2))) #convert 50x50 to 25x25
#25x25 fed in
image_model.add(ZeroPadding2D((2, 2)))
image_model.add(Conv2D(16, (5, 5), activation='relu', name='conv2_1'))
image_model.add(ZeroPadding2D((2, 2)))
image_model.add(Conv2D(16, (5, 5), activation='relu', name='conv2_2'))
image_model.add(MaxPooling2D((5, 5),
strides=(5, 5))) #convert 25x25 to 5x5
#5x5 fed in
image_model.add(ZeroPadding2D((2, 2)))
image_model.add(Conv2D(40, (5, 5), activation='relu', name='conv3_1'))
image_model.add(ZeroPadding2D((2, 2)))
image_model.add(Conv2D(32, (5, 5), activation='relu', name='conv3_2'))
image_model.add(Dropout(0.2))
image_model.add(Flatten())
image_model.add(Dense(512))
image_model.add(Activation('tanh'))
image_model.add(Dropout(0.2))
image_model.add(Dense(512))
image_model.add(Activation('tanh'))
image_model.add(Dropout(0.15))
image_model.add(Dense(512))
image_model.add(Activation('tanh'))
image_model.add(Dropout(0.1))
image_model.add(Dense(512))
image_model.add(Activation('tanh'))
image_model.add(Dense(512))
image_model.add(Activation('tanh'))
image_model.add(Dense(512))
image_model.add(Activation('tanh'))
image_model.add(Dense(512))
image_model.add(Activation('tanh'))
image_model.add(Dense(512))
image_model.add(Activation('tanh'))
image_model.add(Dense(5))
image_model.add(Activation('sigmoid'))
return image_model
'''
