Python distributions.uniform方法代码示例

# 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} 

# 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} 

# 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} 

# 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 

# 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())} 

# 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} 

# 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} 

# 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} 

# 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} 

# 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 

# 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} 

# 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} 

# 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}