本文整理汇总了Python中hyperas.distributions.choice方法的典型用法代码示例。如果您正苦于以下问题:Python distributions.choice方法的具体用法?Python distributions.choice怎么用?Python distributions.choice使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类hyperas.distributions
的用法示例。
在下文中一共展示了distributions.choice方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create_model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def create_model(x_train):
network = scgen.VAEArith(x_dimension=x_train.X.shape[1],
z_dimension={{choice([10, 20, 50, 75, 100])}},
learning_rate={{choice([0.1, 0.01, 0.001, 0.0001])}},
alpha={{choice([0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001])}},
dropout_rate={{choice([0.2, 0.25, 0.5, 0.75, 0.8])}},
model_path=f"./")
result = network.train(x_train,
n_epochs={{choice([100, 150, 200, 250])}},
batch_size={{choice([32, 64, 128, 256])}},
verbose=2,
shuffle=True,
save=False)
best_loss = np.amin(result.history['loss'])
print('Best Loss of model:', best_loss)
return {'loss': best_loss, 'status': STATUS_OK, 'model': network.vae_model}
示例2: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(X_train, Y_train, X_test, Y_test):
model = Sequential()
model.add(Dense({{choice([15, 512, 1024])}},input_dim=8,init='uniform', activation='softplus'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([256, 512, 1024])}}))
model.add(Activation({{choice(['relu', 'sigmoid','softplus'])}}))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense(1, init='uniform', activation='sigmoid'))
model.compile(loss='mse', metrics=['accuracy'],
optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
model.fit(X_train, Y_train,
batch_size={{choice([10, 50, 100])}},
nb_epoch={{choice([1, 50])}},
show_accuracy=True,
verbose=2,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
示例3: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(X_train, X_test, Y_train, Y_test):
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([400, 512, 600])}}))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense(10))
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy'])
nb_epoch = 10
batch_size = 128
model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=2,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
示例4: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(X_train, Y_train, X_test, Y_test):
inputs = Input(shape=(784,))
x = Dense({{choice([20, 30, 40])}}, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=predictions)
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy'])
model.fit(X_train, Y_train,
batch_size={{choice([64, 128])}},
epochs=1,
verbose=2,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
示例5: model_multi_line_arguments
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model_multi_line_arguments(X_train, Y_train,
X_test, Y_test):
inputs = Input(shape=(784,))
x = Dense({{choice([20, 30, 40])}}, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=predictions)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, Y_train,
batch_size={{choice([64, 128])}},
epochs=1,
verbose=2,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
示例6: create_model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def create_model(x_train, y_train, x_test, y_test):
model = Sequential()
model.add(Dense(44, input_shape=(784,)))
model.add(Activation({{choice(['relu', 'sigmoid'])}}))
model.add(Dense(44))
model.add(Activation({{choice(['relu', 'sigmoid'])}}))
model.add(Dense(10))
model.compile(loss='mae', metrics=['mse'], optimizer="adam")
es = EarlyStopping(monitor='val_loss', min_delta=1e-5, patience=10)
rlr = ReduceLROnPlateau(factor=0.1, patience=10)
_ = model.fit(x_train, y_train, epochs=1, verbose=0, callbacks=[es, rlr],
batch_size=24, validation_data=(x_test, y_test))
mae, mse = model.evaluate(x_test, y_test, verbose=0)
print('MAE:', mae)
return {'loss': mae, 'status': STATUS_OK, 'model': model}
示例7: ensemble_model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def ensemble_model(X_train, X_test, Y_train, Y_test):
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([400, 512, 600])}}))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense(10))
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy'])
nb_epoch = 10
batch_size = 128
model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=2,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
示例8: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(x_train, y_train, x_test, y_test):
"""Model providing function:
Create Keras model with double curly brackets dropped-in as needed.
Return value has to be a valid python dictionary with two customary keys:
- loss: Specify a numeric evaluation metric to be minimized
- status: Just use STATUS_OK and see hyperopt documentation if not feasible
The last one is optional, though recommended, namely:
- model: specify the model just created so that we can later use it again.
"""
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
keras_model = Sequential()
keras_model.add(Dense(512, input_shape=(784,)))
keras_model.add(Activation('relu'))
keras_model.add(Dropout({{uniform(0, 1)}}))
keras_model.add(Dense({{choice([256, 512, 1024])}}))
keras_model.add(Activation('relu'))
keras_model.add(Dropout({{uniform(0, 1)}}))
keras_model.add(Dense(10))
keras_model.add(Activation('softmax'))
rms = RMSprop()
keras_model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['acc'])
keras_model.fit(x_train, y_train,
batch_size={{choice([64, 128])}},
epochs=1,
verbose=2,
validation_data=(x_test, y_test))
score, acc = keras_model.evaluate(x_test, y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': keras_model.to_yaml(),
'weights': pickle.dumps(keras_model.get_weights())}
# Create Spark context
示例9: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(x_train, y_train, x_test, y_test):
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
keras_model = Sequential()
keras_model.add(Dense(512, input_shape=(784,)))
keras_model.add(Activation('relu'))
keras_model.add(Dropout({{uniform(0, 1)}}))
keras_model.add(Dense({{choice([256, 512, 1024])}}))
keras_model.add(Activation('relu'))
keras_model.add(Dropout({{uniform(0, 1)}}))
keras_model.add(Dense(10))
keras_model.add(Activation('softmax'))
rms = RMSprop()
keras_model.compile(loss='categorical_crossentropy',
optimizer=rms, metrics=['acc'])
keras_model.fit(x_train, y_train,
batch_size={{choice([64, 128])}},
epochs=1,
verbose=2,
validation_data=(x_test, y_test))
score, acc = keras_model.evaluate(x_test, y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': keras_model.to_yaml(),
'weights': pickle.dumps(keras_model.get_weights())}
示例10: data
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def data(search_params):
global mat
BATCH_SIZE = search_params["batch_size"]# {{choice([20, 30, 40, 50])}}
TIME_STEPS = search_params["time_steps"] # {{choice([30, 60, 90])}}
x_train, x_test = train_test_split(mat, train_size=0.8, test_size=0.2, shuffle=False)
# scale the train and test dataset
min_max_scaler = MinMaxScaler()
x_train = min_max_scaler.fit_transform(x_train)
x_test = min_max_scaler.transform(x_test)
x_train_ts, y_train_ts = build_timeseries(x_train, 3, TIME_STEPS)
x_test_ts, y_test_ts = build_timeseries(x_test, 3, TIME_STEPS)
x_train_ts = trim_dataset(x_train_ts, BATCH_SIZE)
y_train_ts = trim_dataset(y_train_ts, BATCH_SIZE)
print("Train size(trimmed) {}, {}".format(x_train_ts.shape, y_train_ts.shape))
# this is to check if formatting of data is correct
print("{},{}".format(x_train[TIME_STEPS - 1, 3], y_train_ts[0]))
print(str(x_train[TIME_STEPS, 3]), str(y_train_ts[1]))
print(str(x_train[TIME_STEPS + 1, 3]), str(y_train_ts[2]))
print(str(x_train[TIME_STEPS + 2, 3]), str(y_train_ts[3]))
print(str(x_train[TIME_STEPS + 3, 3]), str(y_train_ts[4]))
print(str(x_train[TIME_STEPS + 4, 3]), str(y_train_ts[5]))
print(str(x_train[TIME_STEPS + 5, 3]), str(y_train_ts[6]))
x_test_ts = trim_dataset(x_test_ts, BATCH_SIZE)
y_test_ts = trim_dataset(y_test_ts, BATCH_SIZE)
# example usage of logger. You can try out on better places
logging.debug("Test size(trimmed) {}, {}".format(x_test_ts.shape, y_test_ts.shape))
logging.debug("Are any NaNs present in train/test matrices?{0},{1}".format(str(np.isnan(x_train).any()),
str(np.isnan(x_test).any())))
return x_train_ts, y_train_ts, x_test_ts, y_test_ts
示例11: create_model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def create_model(tr_pairs, tr_y, te_pairs, te_y,input_shape):
epochs = 20
dropout1 = {{uniform(0,1)}}
dropout2 = {{uniform(0,1)}}
dense_filter1 = {{choice([64,128,256])}}
dense_filter2 = {{choice([64,128,256])}}
dense_filter3 = {{choice([64,128,256])}}
# network definition
base_network = create_base_network(input_shape,dense_filter1,dense_filter2,dense_filter3,dropout1,dropout2)
input_a = Input(shape=input_shape)
input_b = Input(shape=input_shape)
processed_a = base_network(input_a)
processed_b = base_network(input_b)
distance = Lambda(euclidean_distance,
output_shape=eucl_dist_output_shape)([processed_a, processed_b])
model = Model([input_a, input_b], distance)
rms = RMSprop()
model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
batch_size=128,
epochs=epochs,
verbose=1,
validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y))
y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
tr_acc = compute_accuracy(tr_y, y_pred)
y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
te_acc = compute_accuracy(te_y, y_pred)
print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
return {'loss': -te_acc, 'status': STATUS_OK, 'model': model}
示例12: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(X_train, Y_train, X_test, Y_test):
'''
Model providing function:
Create Keras model with double curly brackets dropped-in as needed.
Return value has to be a valid python dictionary with two customary keys:
- loss: Specify a numeric evaluation metric to be minimized
- status: Just use STATUS_OK and see hyperopt documentation if not feasible
The last one is optional, though recommended, namely:
- model: specify the model just created so that we can later use it again.
'''
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([256, 512, 1024])}}))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense(10))
model.add(Activation('softmax'))
rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy'])
model.fit(X_train, Y_train,
batch_size={{choice([64, 128])}},
nb_epoch=1,
verbose=2,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
示例13: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(X_train, X_test, y_train, y_test, max_features, maxlen):
model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen))
model.add(LSTM(128))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
early_stopping = EarlyStopping(monitor='val_loss', patience=4)
checkpointer = ModelCheckpoint(filepath='keras_weights.hdf5',
verbose=1,
save_best_only=True)
model.fit(X_train, y_train,
batch_size={{choice([32, 64, 128])}},
nb_epoch=1,
validation_split=0.08,
callbacks=[early_stopping, checkpointer])
score, acc = model.evaluate(X_test, y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
示例14: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(X_train, X_test, y_train, y_test, maxlen, max_features):
embedding_size = 300
pool_length = 4
lstm_output_size = 100
batch_size = 200
nb_epoch = 1
model = Sequential()
model.add(Embedding(max_features, embedding_size, input_length=maxlen))
model.add(Dropout({{uniform(0, 1)}}))
# Note that we use unnamed parameters here, which is bad style, but is used here
# to demonstrate that it works. Always prefer named parameters.
model.add(Convolution1D({{choice([64, 128])}},
{{choice([6, 8])}},
border_mode='valid',
activation='relu',
subsample_length=1))
model.add(MaxPooling1D(pool_length=pool_length))
model.add(LSTM(lstm_output_size))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print('Train...')
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch,
validation_data=(X_test, y_test))
score, acc = model.evaluate(X_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
示例15: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import choice [as 别名]
def model(x_train, y_train, x_test, y_test):
"""
Model providing function:
Create Keras model with double curly brackets dropped-in as needed.
Return value has to be a valid python dictionary with two customary keys:
- loss: Specify a numeric evaluation metric to be minimized
- status: Just use STATUS_OK and see hyperopt documentation if not feasible
The last one is optional, though recommended, namely:
- model: specify the model just created so that we can later use it again.
"""
model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([256, 512, 1024])}}))
model.add(Activation({{choice(['relu', 'sigmoid'])}}))
model.add(Dropout({{uniform(0, 1)}}))
# If we choose 'four', add an additional fourth layer
if {{choice(['three', 'four'])}} == 'four':
model.add(Dense(100))
# We can also choose between complete sets of layers
model.add({{choice([Dropout(0.5), Activation('linear')])}})
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', metrics=['accuracy'],
optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
model.fit(x_train, y_train,
batch_size={{choice([64, 128])}},
epochs=1,
verbose=2,
validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}