def test_mutate_architecture():
model = Sequential([
Conv2D(filters=6,
kernel_size=(3, 3),
activation='relu',
input_shape=(224, 224, 1)), # input shape 1 or 3?
AveragePooling2D(),
Conv2D(filters=16, kernel_size=(3, 3), activation='relu'),
AveragePooling2D(),
Flatten(),
Dense(units=60, activation='relu'
), # TODO change Units because we only have 2 classes to predict
Dense(units=42, activation='relu'
), # TODO change Units because we only have 2 classes to predict
Dense(units=1, activation='sigmoid'
) # TODO change Units because we only have 2 classes to predict
])
indiv = {'architecture': model.get_config()}
print(indiv['architecture'])
mutate_architecture(indiv)
print(indiv['architecture'])
def model_remove_dropout(model_train: keras.Sequential) -> keras.Sequential:
""" Function to remove dropout layer prior to saving Sequential model """
# get layers names and classes
model_layers = model_train.get_config()['layers']
layers_info = {
layer['config']['name']: layer['class_name']
for layer in model_layers
}
if "Dropout" not in layers_info.values():
return model_train
layers_selected = [
layer_name for layer_name, layer_class in layers_info.items()
if layer_class != "Dropout"
]
model = keras.Sequential()
for layer_name in layers_selected:
model.add(model_train.get_layer(name=layer_name))
return model
def trainningNetwork(bagOfWords, y):
tiempo_i = time.time()
Errores = np.ones(10)
# Sens = np.zeros(10)
# Espec = np.zeros(10)
Precision = np.zeros(10)
Recall = np.zeros(10)
F1score = np.zeros(10)
j = 0
kf = KFold(n_splits=10, shuffle=True)
for train_index, test_index in kf.split(bagOfWords):
#print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = bagOfWords[train_index], bagOfWords[test_index]
y_train, y_test = y[train_index], y[test_index]
#Instanciamos el modelo MLP
model = Sequential()
model.add(
Dense(units=15, activation='relu', input_dim=bagOfWords.shape[1]))
#Dropout
model.add(Dropout(0.25))
model.add(Dense(units=50, activation='relu'))
model.add(Dense(units=20, activation='relu'))
model.add(Dense(units=50, activation='relu'))
model.add(Dense(units=40, activation='relu'))
model.add(Dense(units=40, activation='relu'))
model.add(Dense(units=20, activation='relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
# Model config
model.get_config()
# List all weight tensors
model.get_weights()
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#Train process
model.fit(X_train, y_train, epochs=30)
# dump(model, 'mlpModel.joblib')
# Test
ypred = model.predict(X_test)
y_pred = []
for yp, yt in zip(ypred, y_test):
if yp <= 0.5:
yp = 0
else:
yp = 1
y_pred.append(yp)
#print(yp, '\t', yt)
y_pred = np.asarray(y_pred)
Errores[j] = classification_error(y_pred, y_test)
#print('Error en la iteración: ', Errores[j])
precision, recall, f1score = error_measures(y_pred, y_test)
# Sens[j] = sens
# Espec[j] = esp
Precision[j] = precision
Recall[j] = recall
F1score[j] = f1score
j += 1
return model, Errores, Precision, Recall, F1score, time.time() - tiempo_i
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Flatten,Dense,Dropout
# 模型定义
model = Sequential([
Flatten(input_shape=(28, 28)),
Dense(units=200,activation='tanh'),
Dropout(0.4),
Dense(units=100,activation='tanh'),
Dropout(0.4),
Dense(units=10,activation='softmax')
])
# 保存模型结构
config = model.get_config()
print(config)
# In[2]:
# 保存模型结构
json_config = model.to_json()
print(json_config)
# In[3]:
import json
def build_fn(model_struct):
if model_struct == 'LeNet':
model = Sequential([
Conv2D(filters=6,
kernel_size=(3, 3),
activation='relu',
input_shape=(224, 224, 1)), # input shape 1 or 3?
AveragePooling2D(),
Conv2D(filters=16, kernel_size=(3, 3), activation='relu'),
AveragePooling2D(),
Flatten(),
Dense(
units=60, activation='relu'
), # TODO change Units because we only have 2 classes to predict
Dense(
units=42, activation='relu'
), # TODO change Units because we only have 2 classes to predict
Dense(
units=1, activation='sigmoid'
) # TODO change Units because we only have 2 classes to predict
])
return model.get_config()
elif model_struct == 'AlexNet':
model = Sequential([
Conv2D(filters=96,
input_shape=(224, 224, 1),
kernel_size=(11, 11),
activation='relu',
strides=(4, 4),
padding='valid'), # Should input shape be 1 or 3?
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),
Conv2D(filters=256,
kernel_size=(11, 11),
activation='relu',
strides=(1, 1),
padding='valid'),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),
Conv2D(filters=384,
kernel_size=(3, 3),
activation='relu',
strides=(1, 1),
padding='valid'),
Conv2D(filters=384,
kernel_size=(3, 3),
activation='relu',
strides=(1, 1),
padding='valid'),
Conv2D(filters=256,
kernel_size=(3, 3),
activation='relu',
strides=(1, 1),
padding='valid'),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),
Flatten(),
Dense(4096, input_shape=(224 * 224, ), activation='relu'),
Dropout(0.4),
Dense(
4096, activation='relu'
), # TODO change Units because we only have 2 classes to predict
Dropout(0.4),
Dense(
1000, activation='relu'
), # TODO change Units because we only have 2 classes to predict
Dropout(0.4),
Dense(
1, activation='sigmoid'
) # TODO change Units because we only have 2 classes to predict
])
return model.get_config()
elif model_struct == 'Random':
model = Sequential()
model.add(random_conv(224, True))
while True:
rand_val = random.random()
try:
if not any(isinstance(x, Flatten) for x in
model.layers): # if there's not a Flatten layer yet
input_size = min(model.layers[-1].output_shape[1],
model.layers[-1].output_shape[2])
if input_size < 2 or len(model.layers) > 6:
model.add(Flatten())
continue
if rand_val < 0.3:
model.add(random_conv(input_size, False))
elif rand_val < 0.6:
model.add(random_pool(input_size, 'max'))
elif rand_val < 0.9:
model.add(random_pool(input_size, 'average'))
else:
model.add(Flatten())
else: # if there is a Flatten layer already
if rand_val < 0.35:
model.add(random_dropout())
elif rand_val < 0.7:
model.add(random_dense(False))
else:
break
except tensorflow.errors.ResourceExhaustedError:
print("Initial Architecture went OOM, retrying...")
model.add(random_dense(True))
return model.get_config()
print("model was not LeNet, AlexNet, or Random")
