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DenseNet.py
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DenseNet.py
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# -*- coding: utf-8 -*-
# @File : DenseNet.py
# @Author : Hua Guo
# @Time : 2019/12/25 下午9:56
# @Disc :
import tensorflow as tf
from tensorflow.keras import (
layers,
Model
)
import matplotlib.pyplot as plt
from src.BaseClass.DLModel import DLModel
class ConvLayer(DLModel):
def __init__(self, num_channel):
super(ConvLayer, self).__init__()
self.layers_lst.extend([layers.BatchNormalization()
, layers.Activation("relu")
, layers.Conv2D(filters=num_channel, kernel_size=3, padding='same', strides=1)
])
class DenseBlock(DLModel):
def __init__(self, num_conv, num_channel):
super(DenseBlock, self).__init__()
for _ in range(num_conv):
self.layers_lst.append(ConvLayer(num_channel=num_channel))
def call(self, inputs, training=None, mask=None):
X = inputs
# res = X.copy()
for layer in self.layers_lst:
Y = layer(X)
X = tf.concat([X, Y], axis=-1)
return X
class TransitionBlock(DLModel):
def __init__(self, num_channel):
super(TransitionBlock, self).__init__()
self.layers_lst.extend(
[layers.BatchNormalization()
, layers.Activation('relu')
, layers.Conv2D(filters=num_channel, kernel_size=1)
, layers.MaxPool2D(pool_size=2, strides=2)]
)
class DenseNet(DLModel):
def __init__(self, label_num):
super(DenseNet, self).__init__()
channel_num = 64
# first component
self.layers_lst.extend(
[
layers.Conv2D(filters=channel_num, kernel_size=7, strides=2, padding='same')
, layers.BatchNormalization()
, layers.MaxPool2D(pool_size=3, strides=2, padding='same')
]
)
# second: dense and transition block
growth_rate = 32
num_conv_in_dense_block = [4, 4, 4, 4]
for i, num_conv in enumerate(num_conv_in_dense_block):
self.layers_lst.append(DenseBlock(num_channel=growth_rate, num_conv=num_conv))
channel_num += num_conv*growth_rate
if i != len(num_conv_in_dense_block)-1:
channel_num = channel_num//2
self.layers_lst.append(TransitionBlock(num_channel=channel_num))
# third: final component
self.layers_lst.extend(
[
layers.BatchNormalization()
, layers.Activation('relu')
, layers.GlobalAvgPool2D()
, layers.Dense(units=label_num)
]
)
if __name__ == "__main__":
# config
debug = True
img_dimension = 28
target_dimension = 224
batch_size = 8
epochs = 10
label_num = 10
if debug:
train_num = 60
test_num = 10
else:
train_num = -1
test_num = -1
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train = x_train[:train_num, :, :]
y_train = y_train[:train_num, ]
x_test = x_test[:test_num, :, :]
y_test = y_test[:test_num, ]
x_train = (x_train.astype('float32')/225).reshape(x_train.shape[0], img_dimension, img_dimension, 1).astype('float32')
x_test = (x_test.astype('float32')/225).reshape(x_test.shape[0], img_dimension, img_dimension, 1).astype('float32')
x_train = tf.image.resize(x_train, [target_dimension, target_dimension])
x_test = tf.image.resize(x_test, [target_dimension, target_dimension])
model = DenseNet(label_num=10)
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
sample_input = tf.ones(shape=[1, target_dimension, target_dimension, 1])
print(sample_input.shape)
# for layer in model.layers:
# print(layer)
# sample_input = layer(sample_input)
# print(sample_input.shape)
# model(sample_input)
# print(model.summary())
history = model.fit(
x=x_train
, y=y_train
, validation_data=(x_test, y_test)
, epochs=epochs
, batch_size=batch_size
)
print(model.summary())
acc = history.history['accuracy']
val_ac = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)
plt.figure(figsize=(10, 10))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Train acc')
plt.plot(epochs_range, val_ac, label='Val acc')
plt.legend(loc='lower right')
plt.title("Train & val accuracy")
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Train loss')
plt.plot(epochs_range, val_loss, label='Val loss')
plt.legend(loc='upper right')
plt.title("Train & val loss")
plt.show()