def create_model(X_train,y_train,X_val,y_val,X_test,y_test):
epochs = 40
es_patience = 5
lr_patience = 3
dropout = None
depth = 25
nb_dense_block = 3
nb_filter = 16
growth_rate = 18
bn = True
reduction_ = 0.5
bs = 32
lr = 1E-5
opt = {{choice([Adam(lr=1E-5), RMSprop(lr=1E-5),Adadelta(),Adamax(lr=1E-5),Nadam()])}}
weight_file = 'hyperas_dn_lr_optimizer_wt_3Oct_1549.h5'
nb_classes = 1
img_dim = (2,96,96)
n_channels = 2
model = DenseNet(depth=depth, nb_dense_block=nb_dense_block,
growth_rate=growth_rate, nb_filter=nb_filter,
dropout_rate=dropout,activation='sigmoid',
input_shape=img_dim,include_top=True,
bottleneck=bn,reduction=reduction_,
classes=nb_classes,pooling='avg',
weights=None)
model.summary()
model.compile(loss=binary_crossentropy, optimizer=opt, metrics=['accuracy'])
es = EarlyStopping(monitor='val_loss', patience=es_patience,verbose=1)
checkpointer = ModelCheckpoint(filepath=weight_file,verbose=1, save_best_only=True)
lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), cooldown=0, patience=lr_patience, min_lr=0.5e-6,verbose=1)
model.fit(X_train,y_train,
batch_size=bs,
epochs=epochs,
callbacks=[lr_reducer,checkpointer,es],
validation_data=(X_val,y_val),
verbose=2)
score, acc = model.evaluate(X_val,y_val)
print("current val accuracy:%0.3f"%acc)
pred = model.predict(X_val)
auc_score = roc_auc_score(y_val,pred)
print("current auc_score ------------------> %0.3f"%auc_score)
model = load_model(weight_file) #This is the best model
score, acc = model.evaluate(X_val,y_val)
print("Best saved model val accuracy:%0.3f"% acc)
pred = model.predict(X_val)
auc_score = roc_auc_score(y_val,pred)
print("best saved model auc_score ------------------> %0.3f"%auc_score)
return {'loss': -auc_score, 'status': STATUS_OK, 'model': model}
# Create the model (without loading weights)
model = DenseNet(depth,
nb_dense_block,
growth_rate,
nb_filter,
dropout_rate=dropout_rate,
input_shape=img_dim,
weights=None)
print('Model created')
model.summary()
optimizer = Adam(lr=1e-3) # Using Adam instead of SGD to speed up training
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['acc'])
print('Finished compiling')
(trainX, trainY), (testX, testY) = cifar10.load_data()
trainX = trainX.astype('float32')
testX = testX.astype('float32')
trainX /= 255.
testX /= 255.
Y_train = np_utils.to_categorical(trainY, nb_classes)
Y_test = np_utils.to_categorical(testY, nb_classes)
generator = ImageDataGenerator(rotation_range=15,
# Create the model (without loading weights)
model = DenseNet(depth,
nb_dense_block,
growth_rate,
nb_filter,
dropout_rate=dropout_rate,
input_shape=img_dim,
weights=None)
print("Model created")
model.summary()
optimizer = Adam(lr=1e-3) # Using Adam instead of SGD to speed up training
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=["accuracy"])
print("Finished compiling")
(trainX, trainY), (testX, testY) = cifar10.load_data()
trainX = trainX.astype('float32')
testX = testX.astype('float32')
trainX /= 255.
testX /= 255.
Y_train = np_utils.to_categorical(trainY, nb_classes)
Y_test = np_utils.to_categorical(testY, nb_classes)
generator = ImageDataGenerator(rotation_range=15,
def fit_model(X_train, y_train, X_val, y_val):
epochs = 30
#input_shape = (1,96,96)
es_patience = 5
lr_patience = 5
#dense_filter = 512
#dropout = 0.76
dropout1 = None
depth = 13 #40
nb_dense_block = 3
nb_filter = 18
growth_rate = 12
weight_decay = 1E-4
lr = 3E-4
weight_file = 'keras_densenet_simple_wt_28Sept.h5'
nb_classes = 1
img_dim = (2, 96, 96)
n_channels = 2
model = DenseNet(depth=depth,
nb_dense_block=nb_dense_block,
growth_rate=growth_rate,
nb_filter=nb_filter,
dropout_rate=dropout1,
activation='sigmoid',
input_shape=img_dim,
include_top=True,
bottleneck=True,
reduction=0.5,
classes=nb_classes,
pooling='avg',
weights=None)
model.summary()
opt = Adam(lr=lr)
model.compile(loss=binary_crossentropy,
optimizer=opt,
metrics=['accuracy'])
es = EarlyStopping(monitor='val_loss', patience=es_patience, verbose=1)
#es = EarlyStopping(monitor='val_acc', patience=es_patience,verbose=1,restore_best_weights=True)
checkpointer = ModelCheckpoint(filepath=weight_file,
verbose=1,
save_best_only=True)
lr_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=np.sqrt(0.1),
cooldown=0,
patience=lr_patience,
min_lr=0.5e-6,
verbose=1)
model.fit(X_train,
y_train,
batch_size=64,
epochs=epochs,
callbacks=[es, lr_reducer, checkpointer],
validation_data=(X_val, y_val),
verbose=2)
score, acc = model.evaluate(X_val, y_val)
print('current Test accuracy:', acc)
pred = model.predict(X_val)
auc_score = roc_auc_score(y_val, pred)
print("current auc_score ------------------> ", auc_score)
model = load_model(weight_file) #This is the best model
score, acc = model.evaluate(X_val, y_val)
print('Best saved model Test accuracy:', acc)
pred = model.predict(X_val)
auc_score = roc_auc_score(y_val, pred)
print("best saved model auc_score ------------------> ", auc_score)
threshold = 0.6
pred_scores2 = (pred > threshold).astype(int)
test_acc2 = accuracy_score(y_val, pred_scores2)
print('Test accuracy 0.6:', test_acc2)
return auc_score, model
def create_model():
epochs = 4
es_patience = 7
lr_patience = 5
dropout = None
#depth = {{choice([7,13,19,25,31])}}
#nb_dense_block = {{choice([2,3])}}
depth = 7
nb_dense_block = 2
nb_filter = 16
#growth_rate = {{choice([6,10,14,18])}}
growth_rate = 10
bn = True
reduction_ = 0.5
bs = 32
lr = 1E-3 #########################################################CHange file name##########################################
nb_classes = 1
img_dim = (2, 96, 96)
n_channels = 2
model = DenseNet(depth=depth,
nb_dense_block=nb_dense_block,
growth_rate=growth_rate,
nb_filter=nb_filter,
dropout_rate=dropout,
activation='sigmoid',
input_shape=img_dim,
include_top=True,
bottleneck=bn,
reduction=reduction_,
classes=nb_classes,
pooling='avg',
weights=None)
model.summary()
opt = Adam(lr=lr)
model.compile(loss=binary_crossentropy,
optimizer=opt,
metrics=['accuracy'])
"""es = EarlyStopping(monitor='val_loss', patience=es_patience,verbose=1)
#es = EarlyStopping(monitor='val_acc', patience=es_patience,verbose=1,restore_best_weights=True)
checkpointer = ModelCheckpoint(filepath=weight_file,verbose=1, save_best_only=True)
lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), cooldown=0, patience=lr_patience, min_lr=0.5e-6,verbose=1)
model.fit(X_train,y_train,
batch_size=bs,
epochs=epochs,
callbacks=[es,lr_reducer,checkpointer],
validation_data=(X_val,y_val),
verbose=2)
score, acc = model.evaluate(X_test, y_test)
print('current Test accuracy:', acc)
pred = model.predict(X_test)
auc_score = roc_auc_score(y_test,pred)
print("current auc_score ------------------> ",auc_score)"""
"""model = load_model(weight_file) #This is the best model
score, acc = model.evaluate(X_test, y_test)
print('Best saved model Test accuracy:', acc)
pred = model.predict(X_test)
auc_score = roc_auc_score(y_test,pred)
print("best saved model auc_score ------------------> ",auc_score)"""
return model
model = DenseNet(
depth=depth,
nb_dense_block=nb_dense_block,
growth_rate=growth_rate,
nb_filter=nb_filter,
dropout_rate=dropout_rate,
input_shape=img_dim,
weights=None,
)
print("Model created")
model.summary()
optimizer = Adam(lr=1e-3) # Using Adam instead of SGD to speed up training
model.compile(loss="categorical_crossentropy",
optimizer=optimizer,
metrics=["acc"])
print("Finished compiling")
(trainX, trainY), (testX, testY) = cifar10.load_data()
trainX = trainX.astype("float32")
testX = testX.astype("float32")
trainX /= 255.0
testX /= 255.0
Y_train = np_utils.to_categorical(trainY, nb_classes)
Y_test = np_utils.to_categorical(testY, nb_classes)
generator = ImageDataGenerator(rotation_range=15,
def create_model(X_train, y_train, X_val, y_val, X_test, y_test):
epochs = 1
es_patience = 5
lr_patience = 3
dropout = None
depth = {{choice([7, 16, 25, 34])}}
nb_dense_block = {{choice([2, 3, 4])}}
nb_filter = 16
growth_rate = {{choice([6, 14, 22, 30])}}
bn = True
reduction_ = 0.5
bs = 32
lr = {{choice([1E-2, 5E-2, 1E-3, 5E-3, 1E-4, 5E-4, 1E-5, 5E-5])}}
weight_file = 'keras_densenet_simple_wt_02Oct_1300.h5'
nb_classes = 1
img_dim = (2, 96, 96)
n_channels = 2
print("Depth: ", depth, " Growth_rate: ", growth_rate, " Nb_dense_block: ",
nb_dense_block, " lr: ", lr)
model = DenseNet(depth=depth,
nb_dense_block=nb_dense_block,
growth_rate=growth_rate,
nb_filter=nb_filter,
dropout_rate=dropout,
activation='sigmoid',
input_shape=img_dim,
include_top=True,
bottleneck=bn,
reduction=reduction_,
classes=nb_classes,
pooling='avg',
weights=None)
model.summary()
opt = Adam(lr=lr)
model.compile(loss=binary_crossentropy,
optimizer=opt,
metrics=['accuracy'])
es = EarlyStopping(monitor='val_loss', patience=es_patience, verbose=1)
checkpointer = ModelCheckpoint(filepath=weight_file,
verbose=1,
save_best_only=True)
lr_reducer = ReduceLROnPlateau(monitor='val_loss',
factor=np.sqrt(0.1),
cooldown=0,
patience=lr_patience,
min_lr=0.5e-6,
verbose=1)
model.fit(X_train,
y_train,
batch_size=bs,
epochs=epochs,
callbacks=[lr_reducer, es],
validation_data=(X_val, y_val),
verbose=2)
score, acc = model.evaluate(X_val, y_val)
print('current val accuracy:', acc)
pred = model.predict(X_val)
auc_score = roc_auc_score(y_val, pred)
print("current auc_score ------------------> ", auc_score)
#model = load_model(weight_file) #This is the best model
#score, acc = model.evaluate(X_val,y_val)
#print('Best saved model val accuracy:', acc)
#pred = model.predict(X_val)
#auc_score = roc_auc_score(y_val,pred)
#print("best saved model auc_score ------------------> ",auc_score)
return {'loss': -auc_score, 'status': STATUS_OK, 'model': model}
