Exemplos de data em Python

Exemplo n.º 1

Arquivo: example_job.py Projeto: zepx/japanese-handwriting-nn

def load_model_weights(name, model):
    try:
        model.load_weights('weights/M16-hiragana_weights.h5')
    except:
        print "Can't load weights!"


def save_model_weights(name, model):
    try:
        model.save_weights(name)
    except:
        print "failed to save classifier weights"
    pass


X_train, y_train, X_test, y_test = data(mode='hiragana')
n_output = y_train.shape[1]

model = M16(n_output=n_output, input_shape=(1, 64, 64))

load_model_weights('weights/M16-hiragana_weights.h5', model)

adam = Adam(lr=1e-4)
model.compile(loss='categorical_crossentropy', optimizer=adam)

model.fit(X_train, y_train, nb_epoch=20, batch_size=16, show_accuracy=True)

score, acc = model.evaluate(X_test,
                            y_test,
                            batch_size=16,
                            show_accuracy=True,

Exemplo n.º 2

Arquivo: example_job.py Projeto: charlietsai/japanese-handwriting-nn

def load_model_weights(name, model):
    try:
        model.load_weights('weights/M16-hiragana_weights.h5')
    except:
        print "Can't load weights!"


def save_model_weights(name, model):
    try:
        model.save_weights(name)
    except:
        print "failed to save classifier weights"
    pass

X_train, y_train, X_test, y_test = data(mode='hiragana')
n_output = y_train.shape[1]

model = M16(n_output=n_output, input_shape=(1, 64, 64))

load_model_weights('weights/M16-hiragana_weights.h5', model)

adam = Adam(lr=1e-4)
model.compile(loss='categorical_crossentropy', optimizer=adam)

model.fit(X_train, y_train,
          nb_epoch=20,
          batch_size=16,
          show_accuracy=True)

score, acc = model.evaluate(X_test, y_test,

Exemplo n.º 3

    except:
        print("Can't load weights!")


def save_model_weights(name, model):
    try:
        model.save_weights(name)
    except:
        print("failed to save classifier weights")
    pass


img_rows, img_cols = 64, 64

print("Loading training and test data ..")
X_train, y_train, X_test, y_test, input_shape, inv_map = data(mode='kanji')
n_output = y_train.shape[1]
print("Training size: ", X_train.shape[0])
print("Test size: ", X_test.shape[0])
print("Classes: ", n_output)

# setup model
model = M7_1_9(n_output=n_output, input_shape=input_shape)

adam = Adam(lr=1e-4)
model.compile(loss='categorical_crossentropy',
              optimizer=adam,
              metrics=['accuracy'])
model.summary()

# try to load weights

Exemplo n.º 4

Arquivo: example_data.py Projeto: androbi-com/japanese-handwriting-nn

    try:
        model.load_weights(name)
    except:
        print("Can't load weights!")


def save_model_weights(name, model):
    try:
        model.save_weights(name)
    except:
        print("failed to save classifier weights")
    pass


img_rows, img_cols = 64, 64
X_train, y_train, X_test, y_test = data(mode='kanji')
n_output = y_train.shape[1]

# if K.image_dim_ordering() == 'th':
#     X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
#     X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
#     input_shape = (1, img_rows, img_cols)
# else:
#     X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
#     X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
#     input_shape = (img_rows, img_cols, 1)
#
# print ("using shape",input_shape)
# model = M7_1(n_output=n_output, input_shape=input_shape)

# load_model_weights('weights/weights_in.h5', model)