Python Sequential.load_weights方法代码示例

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def initial_num_char_phase1():
    """
    识别二值化图像的字符个数
    :param bw: 二值图像
    :return:
    """
    # 加载模型
    model = Sequential()
    model.add(Convolution2D(4, 5, 5, input_shape=(1, 30, 40), border_mode='valid'))
    model.add(Activation('tanh'))

    model.add(Convolution2D(8, 5, 5, input_shape=(1, 26, 36), border_mode='valid'))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.55))

    model.add(Convolution2D(16, 4, 4, input_shape=(1, 11, 16), border_mode='valid'))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.60))

    model.add(Flatten())
    model.add(Dense(input_dim=16*4*6, output_dim=256, init='glorot_uniform'))
    model.add(Activation('tanh'))

    model.add(Dense(input_dim=256, output_dim=2, init='glorot_uniform'))
    model.add(Activation('softmax'))

    # 加载权值
    model.load_weights('model/train_len_size1.d5')

    sgd = SGD(l2=0.0, lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss='categorical_crossentropy', optimizer=sgd, class_mode="categorical")

    return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def test_nested_sequential(in_tmpdir):
    (x_train, y_train), (x_test, y_test) = _get_test_data()

    inner = Sequential()
    inner.add(Dense(num_hidden, input_shape=(input_dim,)))
    inner.add(Activation('relu'))
    inner.add(Dense(num_class))

    middle = Sequential()
    middle.add(inner)

    model = Sequential()
    model.add(middle)
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=2, validation_split=0.1)
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0)
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, shuffle=False)

    model.train_on_batch(x_train[:32], y_train[:32])

    loss = model.evaluate(x_test, y_test, verbose=0)

    model.predict(x_test, verbose=0)
    model.predict_classes(x_test, verbose=0)
    model.predict_proba(x_test, verbose=0)

    fname = 'test_nested_sequential_temp.h5'
    model.save_weights(fname, overwrite=True)

    inner = Sequential()
    inner.add(Dense(num_hidden, input_shape=(input_dim,)))
    inner.add(Activation('relu'))
    inner.add(Dense(num_class))

    middle = Sequential()
    middle.add(inner)

    model = Sequential()
    model.add(middle)
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
    model.load_weights(fname)
    os.remove(fname)

    nloss = model.evaluate(x_test, y_test, verbose=0)
    assert(loss == nloss)

    # test serialization
    config = model.get_config()
    Sequential.from_config(config)

    model.summary()
    json_str = model.to_json()
    model_from_json(json_str)

    yaml_str = model.to_yaml()
    model_from_yaml(yaml_str)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def test_merge_overlap():
    left = Sequential()
    left.add(Dense(nb_hidden, input_shape=(input_dim,)))
    left.add(Activation('relu'))

    model = Sequential()
    model.add(Merge([left, left], mode='sum'))
    model.add(Dense(nb_class))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=1, validation_data=(X_test, y_test))
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=2, validation_data=(X_test, y_test))
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=2, validation_split=0.1)
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=1, validation_split=0.1)
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, shuffle=False)

    model.train_on_batch(X_train[:32], y_train[:32])

    loss = model.evaluate(X_train, y_train, verbose=0)
    assert(loss < 0.7)
    model.predict(X_test, verbose=0)
    model.predict_classes(X_test, verbose=0)
    model.predict_proba(X_test, verbose=0)
    model.get_config(verbose=0)

    fname = 'test_merge_overlap_temp.h5'
    model.save_weights(fname, overwrite=True)
    model.load_weights(fname)
    os.remove(fname)

    nloss = model.evaluate(X_train, y_train, verbose=0)
    assert(loss == nloss)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def getmodel():
	nb_classes = 2
	model = Sequential()
	model.add(Convolution2D(32, 3, 3, border_mode='same',
	                        input_shape=(1, RECEP_HEI, RECEP_WEI)))
	model.add(Activation('relu'))
	model.add(MaxPooling2D(pool_size=(2, 2)))
	model.add(Convolution2D(32, 3, 3))
	model.add(Activation('relu'))
	model.add(MaxPooling2D(pool_size=(2, 2)))
	model.add(Flatten())
	model.add(Dense(128))
	model.add(Activation('relu'))
	model.add(Dropout(0.5))
	model.add(Dense(nb_classes))
	model.add(Activation('softmax'))

	sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
	model.load_weights('weights.hdf5')
	model.compile(loss='categorical_crossentropy',
	              optimizer=sgd,
	              metrics=['accuracy'])
	# model.fit(X_train, Y_train, batch_size=32, nb_epoch=1,
          # verbose=1, shuffle = True ,validation_split=0.25)
	return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def neural_net(num_sensors, params, load=''):
    model = Sequential()

    # First layer.
    model.add(Dense(
        params[0], init='lecun_uniform', input_shape=(num_sensors,)
    ))
    model.add(Activation('relu'))
    model.add(Dropout(0.2))

    # Second layer.
    model.add(Dense(params[1], init='lecun_uniform'))
    model.add(Activation('relu'))
    model.add(Dropout(0.2))

    # Output layer.
    model.add(Dense(3, init='lecun_uniform'))
    model.add(Activation('linear'))

    rms = RMSprop()
    model.compile(loss='mse', optimizer=rms)

    if load:
        model.load_weights(load)

    return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
    def get_model(self):
        classes = 36
        # data = np.empty((57218, 1, 24, 24), dtype="float32")
        model = Sequential()
        model.add(Convolution2D(4, 5, 5, border_mode='valid', input_shape=(1, 24, 24)))
        model.add(BatchNormalization())
        model.add(Activation('relu'))

        model.add(Convolution2D(8, 3, 3, border_mode='valid'))
        model.add(BatchNormalization())
        model.add(Activation('relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))

        model.add(Convolution2D(16, 3, 3, border_mode='valid'))
        model.add(BatchNormalization())
        model.add(Activation('relu'))
        # model.add(Dropout(0.5))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Flatten())
        model.add(Dense(128, init='normal'))
        model.add(BatchNormalization())
        model.add(Activation('tanh'))
        model.add(Dense(classes, init='normal'))
        model.add(Activation('softmax'))
        sgd = SGD(l2=0.0, lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
        model.compile(loss='categorical_crossentropy', optimizer=sgd, class_mode="categorical")
        model.load_weights("eduLogin/captcha/tmp/weights.11-0.05.h5")
        return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def inference_dense(input_dim, class_num, optimizer='sgd', weights_file=''):
    model = Sequential()
    
    model.add(Dense(2048, input_dim=input_dim))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))

    model.add(Dense(1024))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    
#    model.add(Dense(256))
#    model.add(Activation('relu'))
#    model.add(Dropout(0.5))
    
    model.add(Dense(class_num))
    model.add(Activation('softmax'))
    
    if weights_file:
        model.load_weights(weights_file)
#    adadelta = Adadelta(lr=1.0, rho=0.95, epsilon=1e-06)
    if optimizer == 'sgd':
        opt = SGD(lr=1e-4, decay=1e-6, momentum=0.9, nesterov=True)
    elif optimizer == 'adam':
        opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
    elif optimizer == 'adagrad':
        opt = Adagrad(lr=0.01, epsilon=1e-08)
    elif optimizer == 'adadelta':
        opt = Adadelta(lr=1.0, rho=0.95, epsilon=1e-08)
    elif optimizer == 'rmsprop':
        opt = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08)
    print('compiling model....')
    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
    
    return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def Colorize(weights_path=None):
    model = Sequential()
    # input: 100x100 images with 3 channels -> (3, 100, 100) tensors.
    # this applies 32 convolution filters of size 3x3 each.

    model.add(Convolution2D(512, 1, 1, border_mode='valid',input_shape=(960,224,224)))
    model.add(Activation('relu'))
    model.add(normalization.BatchNormalization())

    model.add(Convolution2D(256, 1, 1, border_mode='valid'))
    model.add(Activation('relu'))
    model.add(normalization.BatchNormalization())

    model.add(Convolution2D(112, 1, 1, border_mode='valid'))
    model.add(Activation('relu'))
    model.add(normalization.BatchNormalization())
   
    print "output shape: ",model.output_shape
    #softmax
    model.add(Reshape((112,224*224)))

    print "output_shape after reshaped: ",model.output_shape
    model.add(Activation('softmax'))

    if weights_path:
        model.load_weights(weights_path)

    return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def model(df, parent_id, go_id):

    # Training
    batch_size = 64
    nb_epoch = 64

    # Split pandas DataFrame
    n = len(df)
    split = 0.8
    m = int(n * split)
    train, test = df[:m], df[m:]


    # train, test = train_test_split(
    #     labels, data, batch_size=batch_size)

    train_label, train_data = train['labels'], train['data']

    if len(train_data) < 100:
        raise Exception("No training data for " + go_id)

    test_label, test_data = test['labels'], test['data']
    test_label_rep = test_label


    train_data = train_data.as_matrix()

    test_data = test_data.as_matrix()
    train_data = numpy.hstack(train_data).reshape(train_data.shape[0], 8000)
    test_data = numpy.hstack(test_data).reshape(test_data.shape[0], 8000)
    shape = numpy.shape(train_data)

    print('X_train shape: ', shape)
    print('X_test shape: ', test_data.shape)
    model = Sequential()
    model.add(Dense(8000, activation='relu', input_dim=8000))
    model.add(Highway())
    model.add(Dense(1, activation='sigmoid'))

    model.compile(
        loss='binary_crossentropy', optimizer='rmsprop', class_mode='binary')

    model_path = DATA_ROOT + parent_id + '/' + go_id + '.hdf5'
    checkpointer = ModelCheckpoint(
        filepath=model_path, verbose=1, save_best_only=True)
    earlystopper = EarlyStopping(monitor='val_loss', patience=7, verbose=1)

    model.fit(
        X=train_data, y=train_label,
        batch_size=batch_size, nb_epoch=nb_epoch,
        show_accuracy=True, verbose=1,
        validation_split=0.2,
        callbacks=[checkpointer, earlystopper])

    # Loading saved weights
    print 'Loading weights'
    model.load_weights(model_path)
    pred_data = model.predict_classes(
        test_data, batch_size=batch_size)
    return classification_report(list(test_label_rep), pred_data)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def make_model_full(inshape, num_classes, weights_file=None):
    model = Sequential()
    model.add(KL.InputLayer(input_shape=inshape[1:]))
    # model.add(KL.Conv2D(32, (3, 3), padding='same', input_shape=inshape[1:]))
    model.add(KL.Conv2D(32, (3, 3), padding='same'))
    model.add(KL.Activation('relu'))
    model.add(KL.Conv2D(32, (3, 3)))
    model.add(KL.Activation('relu'))
    model.add(KL.MaxPooling2D(pool_size=(2, 2)))
    model.add(KL.Dropout(0.25))

    model.add(KL.Conv2D(64, (3, 3), padding='same'))
    model.add(KL.Activation('relu'))
    model.add(KL.Conv2D(64, (3, 3)))
    model.add(KL.Activation('relu'))
    model.add(KL.MaxPooling2D(pool_size=(2, 2)))
    model.add(KL.Dropout(0.25))

    model.add(KL.Flatten())
    model.add(KL.Dense(512))
    model.add(KL.Activation('relu'))
    model.add(KL.Dropout(0.5))
    model.add(KL.Dense(num_classes))
    model.add(KL.Activation('softmax'))

    if weights_file is not None and os.path.exists(weights_file):
        model.load_weights(weights_file)

    return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def temporalNet(weights=None):
    model = Sequential()
    #3D convolutional layer with 32x32 optical flow as input
    model.add(Convolution3D(30, 20, 17, 17, subsample=(4,2,2), input_shape=(1, 120,32,32)))
    model.add(Activation(LeakyReLU()))
    model.add(BatchNormalization())
    model.add(MaxPooling3D(pool_size=(13, 2, 2), strides=(13,2, 2)))
    model.add(Reshape((60, 4, 4)))


    model.add(Convolution2D(100, 3, 3))
    model.add(Activation(LeakyReLU()))
    model.add(BatchNormalization())
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
    model.add(Flatten())


    model.add(Dense(400))
    model.add(Activation(LeakyReLU()))
    model.add(Dropout(0.2))
    model.add(Dense(50))
    model.add(Activation(LeakyReLU()))
    model.add(BatchNormalization())
    model.add(Dense(4, activation='softmax'))

    if weights:
        model.load_weights(weights)

    return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def create_model(load_weights=False):
    nn = Sequential()
    nn.add(Convolution2D(32, 1, 3, 3, border_mode='same', activation='relu'))
    nn.add(Convolution2D(32, 32, 3, 3, border_mode='same', activation='relu'))
    nn.add(MaxPooling2D(poolsize=(2,2)))
    nn.add(Dropout(0.25))
    
    nn.add(Convolution2D(64, 32, 3, 3, border_mode='same', activation='relu')) 
    nn.add(Convolution2D(64, 64, 3, 3, border_mode='same', activation='relu'))
    nn.add(MaxPooling2D(poolsize=(2,2)))
    nn.add(Dropout(0.25))
    
    nn.add(Flatten())
    nn.add(Dense(64*7*7, 256, activation='relu'))
    nn.add(Dropout(0.5))
    
    nn.add(Dense(256,10, activation='softmax'))

    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    nn.compile(loss='categorical_crossentropy', optimizer=sgd)
    
    if load_weights:
        nn.load_weights('cnn_weights.hdf5')
    
    return nn

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def vgg_basic(img_size, weights_path = None, lr = 0.001):
    '''
    INPUT: img_size = size of images to train/ model was trained on
           weights_path = path to get weights of trained model
    OUTPUT: the fitted/unfitted model depending on if a weights path was
            specified

    A basic convolutional neural net. I found this one to have the best results.
    '''
    model = Sequential()

    model.add(ZeroPadding2D((1,1),input_shape=(3, img_size, img_size)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Flatten())
    model.add(Dense(1024, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(5, activation='softmax'))

    if weights_path:
        model.load_weights(weights_path)

    adam = Adam(lr = lr)

    model.compile(optimizer=adam,
                  loss='categorical_crossentropy')

    return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def logistic_regression(model_folder, layer, dimension, number_of_feature,
                        cost="binary_crossentropy", learning_rate=1e-6, dropout_rate=0.5, nepoch=10, activation="relu"):

    model = Sequential()
    model.add(Dense(dimension, input_dim=number_of_feature, init="uniform", activation=activation))
    model.add(Dropout(dropout_rate))

    for idx in range(0, layer-2, 1):
        model.add(Dense(dimension, input_dim=dimension, init="uniform", activation=activation))
        model.add(Dropout(dropout_rate))

    model.add(Dense(1, init="uniform", activation="sigmoid"))

    optimizer = RMSprop(lr=learning_rate, rho=0.9, epsilon=1e-06)
    model.compile(loss=cost, optimizer=optimizer, metrics=['accuracy'])

    filepath_model = get_newest_model(model_folder)
    if filepath_model:
        model.load_weights(filepath_model)

        log("Load weights from {}".format(filepath_model), INFO)
    else:
        log("A new one model, {}".format(model_folder), INFO)

    return model

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import load_weights [as 别名]
def predict_ranking(evalFile, outFile):

    X, qids, pids = load_data(evalFile)
    input_dim = X[0].shape[1]

    assert len(pids[0]) == len(X[0])

    model = Sequential()
    model.add(Dense(64, input_dim=input_dim, init='uniform', activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

    weightsFile = '../model/weights.hdf5'
    model.load_weights(weightsFile)

    Y_p = []
    for x in X:
        Y_p.append(model.predict(x))

    f = open(outFile, 'w')

    for n, qid in enumerate(qids):
        tupes = zip(Y_p[n], pids[n])
        sortedTupes = sorted(tupes, key=lambda x: x[0], reverse=True)
        for n, (y, pid) in enumerate(sortedTupes):
            f.write('{}\tITER\t{}\t{}\t{}\tSOMEID\n'.format(qid, pid, n, 1001-n))