Создайте пользовательский слой для сверточной нейронной сети, но напомните TypeError: аргумент float () должен быть строкой или числом, а не «измерением» - PullRequest
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Создайте пользовательский слой для сверточной нейронной сети, но напомните TypeError: аргумент float () должен быть строкой или числом, а не «измерением»

0 голосов
/ 18 марта 2020

Код ниже (модель уровня клиента частично опущена): 

import tensorflow as tf
print(tf.__version__)
from __future__ import absolute_import, division, print_function
# TensorFlow and tf.keras
#from tensorflow.keras import datasets, layers, models

import numpy as np
import os
import pickle
import gzip
import urllib.request

from keras.optimizers import SGD
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D

from keras.layers import Layer

from keras.utils import np_utils
from keras.models import load_model

# custom layers starts from here.

class STFTLayer(Layer):
    def __init__(self, patch_size, stride, **kwargs):
        super(STFTLayer, self).__init__(**kwargs)
        self.patch_size = patch_size
        self.stride = stride
    # TensorFlow uses this function internally to get the right
    # shape for next layer in the model
    # DO NOT EDIT
    def compute_output_shape(self, input_shape):
        mock_shape = (1, input_shape[1], input_shape[2], input_shape[3])
        mock_data = tf.zeros(mock_shape)
        mock_patches = tf.image.extract_image_patches(mock_data,
        sizes=[1,self.patch_size,self.patch_size,1],strides=[1,self.stride,self.stride,1],rates=[1,1,1,1],padding='VALID')
        op_shape = mock_patches.shape
        return (input_shape[0], op_shape[1], op_shape[2], op_shape[3])
    # TensorFlow uses the this function to save the layer when
    # model.save is called
    # DO NOT EDIT
    def get_config(self):
        config = super(STFTLayer,self).get_config()
        config['patch_size'] = self.patch_size
        config['stride'] = self.stride
        return config
    # Change this function to change how the sampler vectors look
    def call(self, inputs):
        # Get patches from the input batch of images
        # code omitted here
        ###
        return op

# Main code for modeling begins here
# Load MNIST datasets 

def extract_data(filename, num_images):
    with gzip.open(filename) as bytestream:
        bytestream.read(16)
        buf = bytestream.read(num_images*28*28)
        data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
        data = (data / 255) - 0.5
        data = data.reshape(num_images, 28, 28, 1)
        return data

def extract_labels(filename, num_images):
    with gzip.open(filename) as bytestream:
        bytestream.read(8)
        buf = bytestream.read(1 * num_images)
        labels = np.frombuffer(buf, dtype=np.uint8)
    return (np.arange(10) == labels[:, None]).astype(np.float32)

class MNIST:
    def __init__(self):
        if not os.path.exists("data"):
            os.mkdir("data")
            files = ["train-images-idx3-ubyte.gz",
                     "t10k-images-idx3-ubyte.gz",
                     "train-labels-idx1-ubyte.gz",
                     "t10k-labels-idx1-ubyte.gz"]
            for name in files:

                urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/' + name, "data/"+name)

        train_data = extract_data("data/train-images-idx3-ubyte.gz", 60000)
        train_labels = extract_labels("data/train-labels-idx1-ubyte.gz", 60000)
        self.test_data = extract_data("data/t10k-images-idx3-ubyte.gz", 10000)
        self.test_labels = extract_labels("data/t10k-labels-idx1-ubyte.gz", 10000)

        VALIDATION_SIZE = 5000

        self.validation_data = train_data[:VALIDATION_SIZE, :, :, :]
        self.validation_labels = train_labels[:VALIDATION_SIZE]
        self.train_data = train_data[VALIDATION_SIZE:, :, :, :]
        self.train_labels = train_labels[VALIDATION_SIZE:]


print(MNIST().train_data.shape)
print(MNIST().validation_data.shape)


train_images = MNIST().train_data
train_labels = MNIST().train_labels
test_images = MNIST().validation_data
test_labels = MNIST().validation_labels



MODEL_PATH = '/home/bo/Documents/Weibo/STFT_model/models/custom_layers'
# If saved model exists, load it
# If any changes are made to STFTLayer logic, remember
# to delete existing saved model file

if not os.path.isdir('/home/bo/Documents/Weibo/STFT_model/models/'):
    os.makedirs('/home/bo/Documents/Weibo/STFT_model/models/')

if os.path.exists(MODEL_PATH):
    model = load_model(MODEL_PATH, custom_objects={'STFTLayer': STFTLayer})
    model.summary()
# Otherwise create new model.
else:

    model = Sequential()
    # Add our STFTLayer as the first layer with patch_size 4 and stride 1
    model.add(STFTLayer(4,1,input_shape=(28,28,1),name='STFTLayer'))
    model.add(Conv2D(32, (1, 1)))
    #model.add(Conv2D(32, (1, 1), input_shape=(28,28,1)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D((2, 2)))
    model.add(Conv2D(64, (1, 1)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D((2, 2)))
    model.add(Conv2D(64, (1, 1)))
    model.add(Activation('relu'))
    model.add(Flatten())
    model.add(Dense(64))
    model.add(Activation('relu'))
    #model.add(Dense(10, activation = 'softmax'))
    model.add(Dense(10))


    def fn(correct, predicted):
        return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
                                                       logits=predicted/1)

    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

    model.compile(loss=fn,
                  optimizer='adamax',
                  metrics=['accuracy'])

    model.fit(train_images, train_labels,
              batch_size=128,
              validation_data=(test_images, test_labels),
              nb_epoch=20,
              shuffle=True)

    model.summary()
    #model.compile(optimizer='adamax',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
    #model.compile(optimizer='adamax',loss='categorical_crossentropy',metrics=['accuracy'])
    # Train model
    #model.fit(train_images, train_labels, epochs=10, batch_size=128, shuffle=True)
    # Save the model
    model.save(MODEL_PATH)
test_loss, test_acc = model.evaluate(test_images, test_labels)

Код выдал мне ошибку: 

TypeError                                 Traceback (most recent call last)
<ipython-input-3-44798d695c82> in <module>()
    190     # Add our STFTLayer as the first layer with patch_size 4 and stride 1
    191     model.add(STFTLayer(4,1,input_shape=(28,28,1),name='STFTLayer'))
--> 192     model.add(Conv2D(32, (1, 1)))
    193     #model.add(Conv2D(32, (1, 1), input_shape=(28,28,1)))
    194     model.add(Activation('relu'))

5 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py in add(self, layer)
    180                 self.inputs = network.get_source_inputs(self.outputs[0])
    181         elif self.outputs:
--> 182             output_tensor = layer(self.outputs[0])
    183             if isinstance(output_tensor, list):
    184                 raise TypeError('All layers in a Sequential model '

/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
    423                                          'You can build it manually via: '
    424                                          '`layer.build(batch_input_shape)`')
--> 425                 self.build(unpack_singleton(input_shapes))
    426                 self.built = True
    427 

/usr/local/lib/python3.6/dist-packages/keras/layers/convolutional.py in build(self, input_shape)
    139                                       name='kernel',
    140                                       regularizer=self.kernel_regularizer,
--> 141                                       constraint=self.kernel_constraint)
    142         if self.use_bias:
    143             self.bias = self.add_weight(shape=(self.filters,),

/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
    241         if dtype is None:
    242             dtype = self.dtype
--> 243         weight = K.variable(initializer(shape, dtype=dtype),
    244                             dtype=dtype,
    245                             name=name,

/usr/local/lib/python3.6/dist-packages/keras/initializers.py in __call__(self, shape, dtype)
    207             scale /= max(1., fan_out)
    208         else:
--> 209             scale /= max(1., float(fan_in + fan_out) / 2)
    210         if self.distribution == 'normal':
    211             # 0.879... = scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)

TypeError: float() argument must be a string or a number, not 'Dimension'

Но код работает, когда я использую tenSenflow.keras для написания модели нейронных сетей ниже приведен код, который работает хорошо: 

from __future__ import absolute_import, division, print_function
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow.keras import layers, models
import os
# Helper libraries
import numpy as np

import urllib.request
import gzip

# Custom layers starts here.

class STFTLayer(layers.Layer):
    def __init__(self, patch_size, stride, **kwargs):
        super(STFTLayer, self).__init__(**kwargs)
        self.patch_size = patch_size
        self.stride = stride
    # TensorFlow uses this function internally to get the right
    # shape for next layer in the model
    # DO NOT EDIT
    def compute_output_shape(self, input_shape):
        mock_shape = (1, input_shape[1], input_shape[2], input_shape[3])
        mock_data = tf.zeros(mock_shape)
        mock_patches = tf.image.extract_image_patches(mock_data,
        sizes=[1,self.patch_size,self.patch_size,1],strides=[1,self.stride,self.stride,1],rates=[1,1,1,1],padding='VALID')
        op_shape = mock_patches.shape
        return (input_shape[0], op_shape[1], op_shape[2], op_shape[3])
    # TensorFlow uses the this function to save the layer when
    # model.save is called
    # DO NOT EDIT
    def get_config(self):
        config = super(STFTLayer,self).get_config()
        config['patch_size'] = self.patch_size
        config['stride'] = self.stride
        return config
    # Change this function to change how the sampler vectors look
    def call(self, inputs):
        # Get patches from the input batch of images
        # code omitted here
        ###
        return op

# Main code for modeling begins here
# Load MNIST datasets

def extract_data(filename, num_images):
    with gzip.open(filename) as bytestream:
        bytestream.read(16)
        buf = bytestream.read(num_images*28*28)
        data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
        data = (data / 255) - 0.5
        data = data.reshape(num_images, 28, 28, 1)
        return data

def extract_labels(filename, num_images):
    with gzip.open(filename) as bytestream:
        bytestream.read(8)
        buf = bytestream.read(1 * num_images)
        labels = np.frombuffer(buf, dtype=np.uint8)
    return (np.arange(10) == labels[:, None]).astype(np.float32)

class MNIST:
    def __init__(self):
        if not os.path.exists("data"):
            os.mkdir("data")
            files = ["train-images-idx3-ubyte.gz",
                     "t10k-images-idx3-ubyte.gz",
                     "train-labels-idx1-ubyte.gz",
                     "t10k-labels-idx1-ubyte.gz"]
            for name in files:

                urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/' + name, "data/"+name)

        train_data = extract_data("data/train-images-idx3-ubyte.gz", 60000)
        train_labels = extract_labels("data/train-labels-idx1-ubyte.gz", 60000)
        self.test_data = extract_data("data/t10k-images-idx3-ubyte.gz", 10000)
        self.test_labels = extract_labels("data/t10k-labels-idx1-ubyte.gz", 10000)

        VALIDATION_SIZE = 5000

        self.validation_data = train_data[:VALIDATION_SIZE, :, :, :]
        self.validation_labels = train_labels[:VALIDATION_SIZE]
        self.train_data = train_data[VALIDATION_SIZE:, :, :, :]
        self.train_labels = train_labels[VALIDATION_SIZE:]


print(MNIST().train_data.shape)
print(MNIST().validation_data.shape)


train_images = MNIST().train_data
train_labels = MNIST().train_labels
test_images = MNIST().validation_data
test_labels = MNIST().validation_labels


MODEL_PATH = '/home/bo/Documents/Weibo/STFT_model/models/custom_layers'
# If saved model exists, load it
# If any changes are made to STFTLayer logic, remember
# to delete existing saved model file

if not os.path.isdir('/home/bo/Documents/Weibo/STFT_model/models/'):
    os.makedirs('/home/bo/Documents/Weibo/STFT_model/models/')

if os.path.exists(MODEL_PATH):
    model = keras.models.load_model(MODEL_PATH, custom_objects={'STFTLayer': STFTLayer})
    model.summary()
# Otherwise create new model.
else:
    model = models.Sequential()
    # Add our STFTLayer as the first layer with patch_size 4 and stride 1
    model.add(STFTLayer(4,1,input_shape=(28,28,1),name='STFTLayer'))
    model.add(layers.Conv2D(32, (1, 1), activation='relu'))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (1, 1), activation='relu'))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (1, 1), activation='relu'))
    model.add(layers.Flatten())
    model.add(layers.Dense(64, activation='relu'))
    #model.add(layers.Dense(10, activation='softmax'))
    model.add(layers.Dense(10))

    model.summary()

    def fn(correct, predicted):
        return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
                                                       logits=predicted/1)

    sgd = tf.keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

    model.compile(loss=fn,
                  optimizer='adamax',
                  metrics=['accuracy'])

    model.fit(train_images, train_labels,
              batch_size=128,
              validation_data=(test_images, test_labels),
              nb_epoch=10,
              shuffle=True)

    #model.compile(optimizer='adamax',loss='categorical_crossentropy',metrics=['accuracy'])
    # Train model
    #model.fit(train_images, train_labels, epochs=10, batch_size=128, shuffle=True)
    # Save the model
    model.save(MODEL_PATH)
test_loss, test_acc = model.evaluate(test_images, test_labels)

Итак, первая версия кода с использованием импорта keras не работает для уровня клиента , но вторая версия кода - это , работающая с использованием импорта tenorflow.keras. Я хочу, чтобы первая версия кода также соответствовала коду других людей. Нужна ваша помощь, спасибо!

1 Ответ

0 голосов
/ 23 марта 2020

Пользовательский слой был решен путем определения функции необучающего параметра и построения слоев последовательной модели с использованием keras.layers.Lambda, tenorflow версии 1.x.

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