outputs=[output_readm, output_cooccur])
adam = Adam(lr=lr)
model.compile(loss={
'readm': 'categorical_crossentropy',
'cooccur': loss_cooccur
},
optimizer=adam,
loss_weights={
'readm': 1.,
'cooccur': loss_weight
})
auccheckpoint = AUCCheckPoint(filepath=model_path + 'embeding_nn_temp' +
str(job_index) + '.h5',
validation_x=val_x,
validation_y=val_y,
auc_output_idx=[0])
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=10,
min_lr=K.epsilon())
class_weight = {'readm': {0: 1., 1: minor_class_weight}}
hist = model.fit_generator(generator=trn_gen,
validation_data=val_gen,
epochs=100,
verbose=2,
callbacks=[auccheckpoint, reduce_lr],
class_weight=class_weight)
x = Dense(fc_width, activation='relu')(merged)
x = Dropout(dropout)(x)
prediction = Dense(2, activation='softmax', name='prediction')(x)
model = Model(inputs=[input_DX1, input_DX, input_PR, input_demo], outputs=prediction)
X_trn = [DX1_array_trn, DX_mat_trn, PR_mat_trn, demo_mat_trn]
X_val = [DX1_array_val, DX_mat_val, PR_mat_val, demo_mat_val]
X_tst = [DX1_array_tst, DX_mat_tst, PR_mat_tst, demo_mat_tst]
for l in model.layers:
if l.name=='DX_embed' or l.name=='PR_embed' or l.name=='DX1_embed':
l.trainable = False
adam = Adam(lr=lr1)
model.compile(optimizer=adam, loss='binary_crossentropy')
auccheckpoint = AUCCheckPoint(filepath=model_path+'ami_glove_auc_temp1_'+str(job_index)+'.h5', validation_y=Y_val,
validation_x=X_val)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=10, min_lr=K.epsilon())
earlystop = EarlyStopping(monitor='val_loss', patience=20)
class_weight = {0:(Y_trn.shape[0]/sum(Y_trn[:, 0])), 1:(Y_trn.shape[0]/sum(Y_trn[:, 1]))}
hist = model.fit(X_trn, Y_trn, batch_size=batchsize, epochs=50, callbacks=[auccheckpoint, reduce_lr, earlystop],
validation_data=[X_val, Y_val], verbose=2)
model.load_weights(model_path+'ami_glove_auc_temp1_'+str(job_index)+'.h5')
y = model.predict(X_tst, verbose=0)
y_pred = y[:, 1]
fpr, tpr, _ = roc_curve(y_true, y_pred)
roc_auc = auc(fpr, tpr)
auc_freeze_lst.append(roc_auc)
x = base_model.output
x = GlobalAveragePooling2D()(x)
output1 = Dense(2, activation='softmax', name='split0_123')(x)
output2 = Dense(2, activation='softmax', name='split01_23')(x)
output3 = Dense(2, activation='softmax', name='split012_3')(x)
model = Model(inputs=base_model.input, outputs=[output1, output2, output3])
adam = Adam()
model.compile(optimizer=adam, loss='categorical_crossentropy')
checkpointer = ModelCheckpoint(filepath=model_path+'binary3_valloss5.h5', verbose=0, save_best_only=True,
save_weights_only=True)
auccheckpt = AUCCheckPoint(filepath=model_path+'binary3_auc5.h5',
validation_y=val_df[['split0_123', 'split01_23', 'split012_3']].values,
validation_itr=val_itr)
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=5, min_lr=1.e-8)
earlystop = EarlyStopping(monitor='val_loss', patience=30)
class_weight = {'split0_123':{0:len(trn_df)/sum(trn_df.split0_123==0), 1:len(trn_df)/sum(trn_df.split0_123==1)},
'split01_23':{0:len(trn_df)/sum(trn_df.split01_23==0), 1:len(trn_df)/sum(trn_df.split01_23==1)},
'split012_3':{0:len(trn_df)/sum(trn_df.split012_3==0), 1:len(trn_df)/sum(trn_df.split012_3==1)}}
hist = model.fit_generator(trn_itr, steps_per_epoch=trn_itr.n // batch_size, epochs=200,
validation_data=val_itr, validation_steps=val_itr.n // batch_size,
callbacks=[checkpointer, auccheckpt, reduce_lr, earlystop],
verbose=2)
#with open('output/binary3_0528.pkl', 'wb') as f:
# pickle.dump(hist.history, f, -1)
input_other = Input(shape=(other_mat.shape[1], ))
merged = Concatenate(axis=1)([DX1_embed, DX_embed, PR_embed, input_other])
x = Dense(fc_width, activation='relu')(merged)
x = Dropout(dropout)(x)
x = Concatenate(axis=1)([x, hosp_embed])
prediction = Dense(1, activation='sigmoid', name='prediction', use_bias=False)(x)
model = Model(inputs=[input_DX1, input_DX, input_PR, input_hosp, input_other], outputs=prediction)
for l in model.layers:
if l.name=='DX_embed' or l.name=='PR_embed' or l.name=='DX1_embed':
l.trainable = False
adam = Adam(lr=lr1)
model.compile(optimizer=adam, loss='binary_crossentropy')
checkpoint = AUCCheckPoint(filepath=model_path+'temp/{}/inference_temp1.h5'.format(job_index), validation_y=y_val,
validation_x=[DX1_array_val, DX_mat_val, PR_mat_val, hosp_array_val, other_mat_val])
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=10, min_lr=K.epsilon())
earlystop = EarlyStopping(monitor='val_loss', patience=20)
hist = model.fit([DX1_array_trn, DX_mat_trn, PR_mat_trn, hosp_array_trn, other_mat_trn], y_trn,
batch_size=batchsize, epochs=50, callbacks=[checkpoint, reduce_lr, earlystop],
validation_data=[[DX1_array_val, DX_mat_val, PR_mat_val, hosp_array_val, other_mat_val], y_val],
verbose=2)
model.load_weights(model_path+'temp/{}/inference_temp1.h5'.format(job_index))
for l in model.layers:
if l.name=='DX_embed' or l.name=='PR_embed' or l.name=='DX1_embed':
l.trainable = True
adam = Adam(lr=lr2)
model.compile(optimizer=adam, loss='binary_crossentropy')
prediction = Dense(2, activation='softmax')(merged)
model = Model(
inputs=[input_DX1, input_DX, input_PR, input_hosp, input_other],
outputs=prediction)
for l in model.layers:
if l.name == 'DX_embed' or l.name == 'PR_embed' or l.name == 'DX1_embed':
l.trainable = False
adam = Adam(lr=lr1)
model.compile(optimizer=adam, loss='categorical_crossentropy')
auccheckpoint = AUCCheckPoint(
filepath=model_path + 'embeding_nn_sub_temp1_' + str(job_index) +
'.h5',
validation_y=Y_val[:, 1],
validation_x=[
DX1_array_val, DX_mat_val, PR_mat_val, hosp_array_val,
other_mat_val
])
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=10,
min_lr=K.epsilon())
earlystop = EarlyStopping(monitor='val_loss', patience=20)
class_weight = {
0: (Y_trn.shape[0] / sum(Y_trn[:, 0])),
1: (Y_trn.shape[0] / sum(Y_trn[:, 1]))
}
hist = model.fit(
model = Model(
inputs=[input_DX1, input_DX, input_PR, input_hosp, input_other],
outputs=prediction)
for l in model.layers:
if l.name == 'DX_embed' or l.name == 'PR_embed' or l.name == 'DX1_embed':
l.trainable = False
adam = Adam(lr=lr1)
model.compile(optimizer=adam, loss='binary_crossentropy')
if checkpoint_monitor == 'auc':
checkpoint = AUCCheckPoint(
filepath=model_path + 'ami_glove_auc_temp1_' + str(job_index) +
'.h5',
validation_y=y_val,
validation_x=[
DX1_array_val, DX_mat_val, PR_mat_val, hosp_array_val,
other_mat_val
])
elif checkpoint_monitor == 'valloss':
checkpoint = ModelCheckpoint(
filepath=model_path + 'ami_glove_auc_temp1_' + str(job_index) +
'.h5',
save_best_only=True,
save_weights_only=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=10,
min_lr=K.epsilon())
earlystop = EarlyStopping(monitor='val_loss', patience=20)
loss_weights={
'CD_Active_AnyLocation': 1.,
'Fistula_Any': 1.,
'Abscess_any': 1.
})
#parallel_model.compile(optimizer='adam', loss={'Abscess_any':'binary_crossentropy'}, metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath=model_path +
'dense121_gr12_3output0611.h5',
verbose=0,
save_best_only=True,
save_weights_only=True)
auccheckpoint = AUCCheckPoint(
filepath=model_path + 'dense121_gr12_3output_auc0611.h5',
validation_y=val_df[[
'CD_Active_AnyLocation', 'Fistula_Any', 'Abscess_any'
]].values,
validation_itr=val_itr)
reduce_lr = ReduceLROnPlateau(monitor='loss',
factor=0.2,
patience=5,
min_lr=1.e-7)
earlystop = EarlyStopping(monitor='val_loss', patience=30)
hist = parallel_model.fit_generator(
trn_itr,
steps_per_epoch=trn_itr.n // (batch_size * G),
epochs=200,
validation_data=val_itr,
validation_steps=val_itr.n // (batch_size * G),
callbacks=[checkpointer, auccheckpoint, reduce_lr, earlystop],
output2 = Dense(1, activation='sigmoid', name='Inflamm_Mayo_2')(x)
output3 = Dense(1, activation='sigmoid', name='Inflamm_Mayo_3')(x)
model = Model(inputs=base_model.input,
outputs=[output0, output1, output2, output3])
adam = Adam()
model.compile(optimizer=adam, loss='binary_crossentropy')
checkpointer = ModelCheckpoint(filepath=model_path + 'binary4_valloss_0528.h5',
verbose=0,
save_best_only=True,
save_weights_only=True)
auccheckpt = AUCCheckPoint(filepath=model_path + 'binary4_auc_0528.h5',
validation_y=val_df[[
'Inflamm_Mayo_0', 'Inflamm_Mayo_1',
'Inflamm_Mayo_2', 'Inflamm_Mayo_3'
]].values,
validation_itr=val_itr)
reduce_lr = ReduceLROnPlateau(monitor='loss',
factor=0.2,
patience=5,
min_lr=1.e-8)
earlystop = EarlyStopping(monitor='val_loss', patience=30)
#class_weights = {0:(5895/4467.), 1:(5895./828), 2:(5895./389), 3:(5895./211)}
hist = model.fit_generator(
trn_itr,
steps_per_epoch=trn_itr.n // batch_size,
epochs=200,
validation_data=val_itr,
merged = Dense(fc_width, activation='relu')(merged)
merged = Dropout(dropout)(merged)
prediction = Dense(2, activation='softmax', name='prediction')(merged)
model = Model(inputs=[
input_DX1, input_DX, input_PR, input_hosp, input_other,
input_labels_flatten, input_conditions
],
outputs=[prediction, cr])
adam = Adam(lr=lr1)
model.compile(optimizer=adam, loss=[K.binary_crossentropy, zero_loss])
auccheckpoint = AUCCheckPoint(
filepath=model_path + 'skipgram_temp' + str(job_index) + '.h5',
validation_y=[Y_val, np.random.randn(Y_val.shape[0], 1)],
validation_x=[
DX1_array_val, DX_mat_val, PR_mat_val, hosp_array_val, other_mat_val,
labels_flatten_val, conditions_val
],
auc_output_idx=[0])
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=10,
min_lr=K.epsilon())
earlystop = EarlyStopping(monitor='val_loss', patience=30)
class_weight = {
0: (Y_trn.shape[0] / sum(Y_trn[:, 0])),
1: (Y_trn.shape[0] / sum(Y_trn[:, 1]))
}
hist = model.fit([
base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
output1 = Dense(2, activation='softmax', name='split0_123')(x)
output2 = Dense(2, activation='softmax', name='split01_23')(x)
output3 = Dense(2, activation='softmax', name='split012_3')(x)
model = Model(inputs=base_model.input, outputs=[output1, output2, output3])
model.compile(optimizer='adam', loss='categorical_crossentropy')
checkpointer = ModelCheckpoint(filepath=model_path + 'binary3_valloss_all.h5',
verbose=0,
save_best_only=True,
save_weights_only=True)
auccheckpt = AUCCheckPoint(filepath=model_path + 'binary3_auc_all.h5',
validation_itr=val_itr)
reduce_lr = ReduceLROnPlateau(monitor='loss',
factor=0.2,
patience=5,
min_lr=K.epsilon())
earlystop = EarlyStopping(monitor='val_loss', patience=30)
class_weight = {
'split0_123': {
0: len(train_df) / sum(train_df.split0_123 == 0),
1: len(train_df) / sum(train_df.split0_123 == 1)
},
'split01_23': {
0: len(train_df) / sum(train_df.split01_23 == 0),
1: len(train_df) / sum(train_df.split01_23 == 1)
},
input_code = Input(shape=(len(code_cat), ))
input_hosp = Input(shape=(len(hosp_cat), ))
input_other = Input(shape=(other_mat_train.shape[1], ))
merged = Concatenate(axis=1)([input_code, input_hosp, input_other])
x = Dense(fc_width1, activation='relu')(merged)
x = Dense(fc_width2, activation='relu')(x)
x = Dropout(dropout)(x)
prediction = Dense(2, activation='softmax')(x)
model = Model(inputs=[input_code, input_hosp, input_other],
outputs=prediction)
adam = Adam(lr=lr)
model.compile(optimizer=adam, loss='categorical_crossentropy')
auccheckpoint = AUCCheckPoint(
filepath=model_path + 'ohe_temp1_' + str(job_index) + '.h5',
validation_y=Y_val[:, 1],
validation_x=[code_ohe_val, hosp_ohe_val, other_mat_val])
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=5,
min_lr=K.epsilon())
earlystop = EarlyStopping(monitor='val_loss', patience=10)
#class_weight = {0:(Y_trn.shape[0]/sum(Y_trn[:, 0])), 1:(Y_trn.shape[0]/sum(Y_trn[:, 1]))}
class_weight = {0: 1., 1: 1.}
hist = model.fit(
[code_ohe_trn, hosp_ohe_trn, other_mat_trn],
Y_trn,
batch_size=batchsize,
epochs=10,
output1 = Dense(2, activation='softmax', name='split0_123')(x)
output2 = Dense(2, activation='softmax', name='split01_23')(x)
output3 = Dense(2, activation='softmax', name='split012_3')(x)
model = Model(inputs=base_model.input, outputs=[output1, output2, output3])
adam = Adam()
model.compile(optimizer=adam, loss='categorical_crossentropy')
checkpointer = ModelCheckpoint(
filepath=model_path + 'weighted_valloss{}{}.h5'.format(tst_seed, val_seed),
verbose=0,
save_best_only=True,
save_weights_only=True)
auccheckpt = AUCCheckPoint(filepath=model_path +
'weighted_auc{}{}.h5'.format(tst_seed, val_seed),
validation_itr=val_itr)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=15,
min_lr=K.epsilon())
earlystop = EarlyStopping(monitor='val_loss', patience=30)
class_weight = {
'split0_123': {
0: sum(trn_df.split0_123 == 1) / len(trn_df),
1: sum(trn_df.split0_123 == 0) / len(trn_df)
},
'split01_23': {
0: sum(trn_df.split01_23 == 1) / len(trn_df) * 2,
1: sum(trn_df.split01_23 == 0) / len(trn_df) * 2
