def dnn():
model = Sequential()
model.add(Dense(64, input_shape=(48 * 48, 1)))
model.add(Dense(64, name='fc1'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(64, name='fc2'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(64, name='fc3'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(64, name='fc4'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(nb_class, activation='softmax', name='fc5'))
model.compile(loss='categorical_crossentropy',
optimizer="adam",
metrics=['accuracy'])
return model
def vgg():
model = Sequential()
model.add(ZeroPadding2D(padding=(1, 1), input_shape=(48, 48, 1)))
model.add(Convolution2D(64, 3, 3, name='conv1_1'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(ZeroPadding2D(padding=(1, 1)))
model.add(Convolution2D(64, 3, 3, name='conv1_2'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Dropout(0.2))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, name='conv2_1'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(128, 3, 3, name='conv2_2'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Dropout(0.2))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, name='conv3_1'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(256, 3, 3, name='conv3_2'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Dropout(0.2))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, name='conv4_1'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(512, 3, 3, name='conv4_2'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(1024, name='fc5'))
model.add(advanced_activations.PReLU(init='zero', weights=None))
model.add(normalization.BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(nb_class, activation='softmax', name='fc6'))
model.compile(loss='categorical_crossentropy',
optimizer="adam",
metrics=['accuracy'])
return model
def cnn_model_one():
model = Sequential()
# main layers
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3)))
# model.add(Activation('relu'))
model.add(advanced_activations.PReLU())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
# model.add(Activation('relu'))
model.add(advanced_activations.PReLU())
model.add(Conv2D(32, 3))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(64, (3, 3)))
# model.add(Activation('relu'))
model.add(advanced_activations.PReLU())
model.add(Conv2D(64, (3, 3)))
# model.add(Activation('relu'))
model.add(advanced_activations.PReLU())
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(64, (3, 3)))
# model.add(Activation('relu'))
model.add(advanced_activations.PReLU())
model.add(Conv2D(64, (3, 3)))
# model.add(Activation('relu'))
model.add(advanced_activations.PReLU())
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3)))
# model.add(Activation('relu'))
model.add(advanced_activations.PReLU())
model.add(Conv2D(128, (3, 3)))
# model.add(Activation('relu'))
model.add(advanced_activations.PReLU())
model.add(MaxPooling2D(pool_size=(2, 2)))
# dense layers
model.add(Flatten())
model.add(Dense(64, kernel_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l1(0.01)))
model.add(advanced_activations.PReLU())
model.add(Dense(32))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.6))
model.add(Dense(30))
model.add(Activation('softmax'))
return model
def CONV(inputs, n_filter):
conv = Conv2D(n_filter, (5, 5),
padding='same',
data_format='channels_first')(inputs)
conv = BatchNormalization()(conv)
conv = advanced_activations.PReLU()(conv)
conv = Conv2D(n_filter, (5, 5),
padding='same',
data_format='channels_first')(conv)
return conv
def fit(self, X, y):
train_X = self.scaler.fit_transform(X.values.astype(float))
try:
train_X = train_X.toarray()
except:
pass
train_y = preprocessing.OneHotEncoder(sparse=False,
n_values=9).fit_transform(
list([[x] for x in y]))
self.nn = kermod.Sequential()
self.nn.add(kerlay.Dropout(0.1))
self.nn.add(kerlay.Dense(train_X.shape[1], 1024,
init='glorot_uniform'))
self.nn.add(keradv.PReLU(1024, ))
self.nn.add(kernorm.BatchNormalization((1024, ), mode=1))
self.nn.add(kerlay.Dropout(0.5))
self.nn.add(kerlay.Dense(1024, 512, init='glorot_uniform'))
self.nn.add(keradv.PReLU(512, ))
self.nn.add(kernorm.BatchNormalization((512, ), mode=1))
self.nn.add(kerlay.Dropout(0.5))
self.nn.add(kerlay.Dense(512, 256, init='glorot_uniform'))
self.nn.add(keradv.PReLU(256, ))
self.nn.add(kernorm.BatchNormalization((256, ), mode=1))
self.nn.add(kerlay.Dropout(0.5))
self.nn.add(
kerlay.Dense(256, 9, init='glorot_uniform', activation='softmax'))
self.nn.compile(loss='categorical_crossentropy', optimizer='adam')
# shuffle the training set
sh = np.array(list(range(len(train_X))))
np.random.shuffle(sh)
train_X = train_X[sh]
train_y = train_y[sh]
self.nn.fit(train_X, train_y, nb_epoch=60, batch_size=2048, verbose=0)
# help="path to output training plot")
args = vars(ap.parse_args())
print(config.LRFIND_PLOT_PATH)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
img_name = 'hepa'
img_size = (96, 96, 3)
model_name = '96-re-anno-hepatocyte-54'
base = Xception(weights="imagenet",
include_top=False,
pooling='max',
input_shape=img_size)
top_model = Sequential()
top_model.add(base)
top_model.add(Dropout(0.5))
top_model.add(Dense(96, name="dense", kernel_initializer=Orthogonal()))
top_model.add(advanced_activations.PReLU(alpha_initializer='zeros'))
top_model.add(Dropout(0.5))
top_model.add(Dense(3, activation='softmax', kernel_initializer=Orthogonal()))
# parallel_model = multi_gpu_model(top_model, 2)
top_model.summary()
# plot_model(top_model,to_file='ss.png')
# top_model.load_weights('./Xception_decay.hdf5')
for layer in top_model.layers:
layer.trainable = True
LearningRate = 0.0001
n_epochs = 30
sgd = optimizers.SGD(lr=config.MIN_LR, momentum=0.9)
adam = optimizers.Adam(lr=LearningRate,
decay=LearningRate / n_epochs,
amsgrad=True)
def add_nn_Layers(model):
reg = 0.0001
dropout = 0.2
model.add(
Conv2D(32, (3, 3),
input_shape=(40, 67, 3),
kernel_initializer='he_uniform',
kernel_regularizer=regularizers.l2(reg))) #200, 350 ; #30, 52
model.add(Dropout(dropout))
model.add(
advanced_activations.PReLU(weights=None, alpha_initializer='zero'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
#
model.add(
Conv2D(32, (3, 3),
kernel_initializer='he_uniform',
kernel_regularizer=regularizers.l2(reg)))
model.add(
advanced_activations.PReLU(weights=None, alpha_initializer='zero'))
model.add(Dropout(dropout))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
#
#
model.add(
Conv2D(16, (3, 3),
kernel_initializer='he_uniform',
kernel_regularizer=regularizers.l2(reg)))
model.add(
advanced_activations.PReLU(weights=None, alpha_initializer='zero'))
model.add(Dropout(dropout))
model.add(BatchNormalization())
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(
Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(
Dense(256,
activation='relu',
kernel_initializer='he_uniform',
kernel_regularizer=regularizers.l2(reg)))
model.add(
advanced_activations.PReLU(weights=None, alpha_initializer='zero'))
model.add(Dropout(dropout))
model.add(BatchNormalization())
model.add(
Dense(128,
kernel_initializer='he_uniform',
kernel_regularizer=regularizers.l2(reg)))
model.add(
advanced_activations.PReLU(weights=None, alpha_initializer='zero'))
model.add(Dropout(dropout))
model.add(BatchNormalization())
model.add(
Dense(122,
activation='softmax',
kernel_initializer='he_uniform',
kernel_regularizer=regularizers.l2(reg))
) #Output layer, so 121 since 0 to 120 stars bnoth 0 and 120 inclusive, last class if for non star-numbers
return model
y_test = np.asarray(taggs_test)
y_train = to_categorical(y_train, num_classes=None)
y_test = to_categorical(y_test, num_classes=None)
# Convolutional model
submodels = []
for kw in size_filters:
submodel = Sequential()
submodel.add(
Conv1D(num_filters,
kw,
padding='valid',
kernel_initializer=initializers.RandomNormal(np.sqrt(2 / kw)),
input_shape=(tam_fijo, embedding_vecor_length)))
submodel.add(advanced_activations.PReLU(initializers.Constant(value=0.25)))
submodel.add(GlobalMaxPooling1D())
submodels.append(submodel)
model = Sequential()
model.add(Merge(submodels, mode="concat"))
model.add(Dropout(dropout))
model.add(Dense(3, activation='softmax'))
# Log to tensorboard
tensorBoardCallback = TensorBoard(log_dir='./logs22', write_graph=True)
adadelta = optimizers.Adadelta(lr=alpha)
model.compile(loss='categorical_crossentropy',
optimizer=adadelta,
metrics=['accuracy', 'mse'])
trainX = trainData[:, 1:].astype(np.float32) trainY = kutils.to_categorical(trainData[:, 0]) noFeatures = trainX.shape[1] scaler = preproc.StandardScaler() trainX = scaler.fit_transform(trainX) """ Final Model """ epochs = 8 nn = models.Sequential() nn.add(core.Dense(noFeatures, input_shape=(noFeatures, ))) nn.add(advact.PReLU()) nn.add(norm.BatchNormalization()) nn.add(core.Dropout(0.2)) nn.add(core.Dense(2 * noFeatures, )) nn.add(advact.PReLU()) nn.add(norm.BatchNormalization()) nn.add(core.Dropout(0.25)) nn.add(core.Dense(noFeatures, )) nn.add(advact.PReLU()) nn.add(norm.BatchNormalization()) nn.add(core.Dropout(0.2)) nn.add(core.Dense(noOfClasses, activation="softmax"))
def models():
extra_params_kaggle_cla = {
'n_estimators': 1200,
'max_features': 30,
'criterion': 'entropy',
'min_samples_leaf': 2,
'min_samples_split': 2,
'max_depth': 30,
'min_samples_leaf': 2,
'n_jobs': nthread,
'random_state': seed
}
extra_params_kaggle_reg = {
'n_estimators': 1200,
'max_features': 30,
'criterion': 'mse',
'min_samples_leaf': 2,
'min_samples_split': 2,
'max_depth': 30,
'min_samples_leaf': 2,
'n_jobs': nthread,
'random_state': seed
}
xgb_reg = {
'objective': 'reg:linear',
'max_depth': 11,
'learning_rate': 0.01,
'subsample': .9,
'n_estimators': 10000,
'colsample_bytree': 0.45,
'nthread': nthread,
'seed': seed
}
xgb_cla = {
'objective': 'binary:logistic',
'max_depth': 11,
'learning_rate': 0.01,
'subsample': .9,
'n_estimators': 10000,
'colsample_bytree': 0.45,
'nthread': nthread,
'seed': seed
}
#NN params
nb_epoch = 3
batch_size = 128
esr = 402
param1 = {
'hidden_units': (256, 256),
'activation': (advanced_activations.PReLU(),
advanced_activations.PReLU(), core.activations.sigmoid),
'dropout': (0., 0.),
'optimizer':
RMSprop(),
'nb_epoch':
nb_epoch,
}
param2 = {
'hidden_units': (1024, 1024),
'activation': (advanced_activations.PReLU(),
advanced_activations.PReLU(), core.activations.sigmoid),
'dropout': (0., 0.),
'optimizer':
RMSprop(),
'nb_epoch':
nb_epoch,
}
clfs = [
(D2, XGBClassifier(**xgb_cla)),
(D11, XGBClassifier(**xgb_cla)),
(D2, XGBRegressor(**xgb_reg)),
(D11, XGBRegressor(**xgb_reg)),
(D2, ensemble.ExtraTreesClassifier(**extra_params_kaggle_cla)),
(D11, ensemble.ExtraTreesClassifier(**extra_params_kaggle_cla)),
(D2, ensemble.ExtraTreesRegressor(**extra_params_kaggle_reg)),
(D11, ensemble.ExtraTreesRegressor(**extra_params_kaggle_reg)),
# (D1, NN(input_dim=D1[0].shape[1], output_dim=1, batch_size=batch_size, early_stopping_epoch=esr, verbose=2, loss='binary_crossentropy', class_mode='binary', **param1)),
# (D3, NN(input_dim=D3[0].shape[1], output_dim=1, batch_size=batch_size, early_stopping_epoch=esr, verbose=2,loss='binary_crossentropy', class_mode='binary', **param1)),
# (D5, NN(input_dim=D5[0].shape[1], output_dim=1, batch_size=batch_size, early_stopping_epoch=esr, verbose=2,loss='binary_crossentropy', class_mode='binary', **param1)),
#
# (D1, NN(input_dim=D1[0].shape[1], output_dim=1, batch_size=batch_size, early_stopping_epoch=esr, verbose=2,loss='binary_crossentropy', class_mode='binary', **param2)),
# (D3, NN(input_dim=D3[0].shape[1], output_dim=1, batch_size=batch_size, early_stopping_epoch=esr, verbose=2,loss='binary_crossentropy', class_mode='binary', **param2)),
# (D5, NN(input_dim=D5[0].shape[1], output_dim=1, batch_size=batch_size, early_stopping_epoch=esr, verbose=2,loss='binary_crossentropy', class_mode='binary', **param2))
]
for clf in clfs:
yield clf
