本文整理汇总了Python中hyperas.distributions.uniform方法的典型用法代码示例。如果您正苦于以下问题:Python distributions.uniform方法的具体用法?Python distributions.uniform怎么用?Python distributions.uniform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.uniform方法的13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: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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2: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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3: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [as 别名]
def model(X_train, Y_train, X_test, Y_test):
model = Sequential()
model.add(Dense(50, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Dense({{choice([20, 30, 40])}}))
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])}},
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4: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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5: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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6: create_model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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7: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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8: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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9: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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))
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',
optimizer={{choice(['rmsprop', 'adam', 'sgd'])}},
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10: create_model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [as 别名]
def create_model(x_train, y_train, x_test, y_test):
"""
Create your model...
"""
layer_1_size = {{quniform(12, 256, 4)}}
l1_dropout = {{uniform(0.001, 0.7)}}
params = {
'l1_size': layer_1_size,
'l1_dropout': l1_dropout
}
num_classes = 10
model = Sequential()
model.add(Dense(int(layer_1_size), activation='relu'))
model.add(Dropout(l1_dropout))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, verbose=0)
out = {
'loss': -acc,
'score': score,
'status': STATUS_OK,
'model_params': params,
}
# optionally store a dump of your model here so you can get it from the database later
temp_name = tempfile.gettempdir()+'/'+next(tempfile._get_candidate_names()) + '.h5'
model.save(temp_name)
with open(temp_name, 'rb') as infile:
model_bytes = infile.read()
out['model_serial'] = model_bytes
return out
示例11: model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [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12: create_model
# 需要导入模块: from hyperas import distributions [as 别名]
# 或者: from hyperas.distributions import uniform [as 别名]
def create_model(train_generator, validation_generator):
l2_reg = regularizers.l2({{loguniform(log(1e-6), log(1e-2))}})
base_model = InceptionResNetV2(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout({{uniform(0, 1)}})(x)
x = Dense(1024, activation='relu', kernel_regularizer=l2_reg, activity_regularizer=l2_reg)(x)
x = Dropout({{uniform(0, 1)}})(x)
predictions = Dense(num_classes, activation='softmax', kernel_regularizer=l2_reg, activity_regularizer=l2_reg)(x)
model = Model(inputs=base_model.input, outputs=predictions)
model_weights_path = os.path.join('models', best_model)
model.load_weights(model_weights_path)
for i in range(int(len(base_model.layers) * {{uniform(0, 1)}})):
layer = base_model.layers[i]
layer.trainable = False
adam = keras.optimizers.Adam(lr={{loguniform(log(1e-6), log(1e-3))}})
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=adam)
# print(model.summary())
model.fit_generator(
train_generator,
steps_per_epoch=num_train_samples // batch_size,
validation_data=validation_generator,
validation_steps=num_valid_samples // batch_size)
score, acc = model.evaluate_generator(validation_generator)
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 uniform [as 别名]
def model(datagen, X_train, Y_train, X_test, Y_test):
batch_size = 32
nb_epoch = 200
# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same',
input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout({{uniform(0, 1)}}))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
# fit the model on the batches generated by datagen.flow()
model.fit_generator(datagen.flow(X_train, Y_train,
batch_size=batch_size),
samples_per_epoch=X_train.shape[0],
nb_epoch=nb_epoch,
validation_data=(X_test, Y_test))
score, acc = model.evaluate(X_test, Y_test, verbose=0)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}