import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sklearn
import sys
import tensorflow as tf
import time
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, multiply, Permute, Concatenate, Conv2D, Add, Activation, Lambda
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
print(module.__name__, module.__version__)
# %%
train_dir = "./10monkeys/training/training"
valid_dir = "./10monkeys/validation/validation"
label_file = "./10monkeys/monkey_labels.txt"
print(os.path.exists(train_dir))
print(os.path.exists(valid_dir))
print(os.path.exists(label_file))
print(os.listdir(train_dir))
print(os.listdir(valid_dir))
labels = pd.read_csv(label_file, header=0)
print(labels)
height = 224
width = 224
channels = 3
batch_size = 24
num_classes = 10
train_datagen = keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=keras.applications.resnet50.preprocess_input,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest',
)
train_generator = train_datagen.flow_from_directory(train_dir,
target_size=(height, width),
batch_size=batch_size,
seed=7,
shuffle=True,
class_mode="categorical")
valid_datagen = keras.preprocessing.image.ImageDataGenerator(
preprocessing_function=keras.applications.resnet50.preprocess_input)
valid_generator = valid_datagen.flow_from_directory(valid_dir,
target_size=(height, width),
batch_size=batch_size,
seed=7,
shuffle=False,
class_mode="categorical")
train_num = train_generator.samples
valid_num = valid_generator.samples
print(train_num, valid_num)
for i in range(2):
x, y = train_generator.next()
print(x.shape, y.shape)
print(y)
def cbam(inputs):
inputs_channels = int(inputs.shape[-1])
x = tf.keras.layers.GlobalAveragePooling2D()(inputs)
x = tf.keras.layers.Dense(int(inputs_channels / 4))(x)
x = tf.keras.layers.Activation("relu")(x)
x = tf.keras.layers.Dense(int(inputs_channels))(x)
x = tf.keras.layers.Activation("softmax")(x)
x = tf.keras.layers.Reshape((1, 1, inputs_channels))(x)
x = tf.keras.layers.multiply([inputs, x])
return x
def Xception(input_shape=(299, 299, 3), classes=10):
"""Instantiates the Xception architecture.
Note that the default input image size for this model is 299x299.
# Arguments
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(299, 299, 3)`.
It should have exactly 3 inputs channels,
and width and height should be no smaller than 71.
E.g. `(150, 150, 3)` would be one valid value.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True,
and if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
RuntimeError: If attempting to run this model with a
backend that does not support separable convolutions.
"""
img_input = layers.Input(shape=input_shape)
channel_axis = 1 if tf.keras.backend.image_data_format() == 'channels_first' else -1
x = layers.Conv2D(32, (3, 3),
strides=(2, 2),
use_bias=False,
name='block1_conv1')(img_input)
x = layers.BatchNormalization(axis=channel_axis, name='block1_conv1_bn')(x)
x = layers.Activation('relu', name='block1_conv1_act')(x)
x = layers.Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block1_conv2_bn')(x)
x = layers.Activation('relu', name='block1_conv2_act')(x)
residual = layers.Conv2D(128, (1, 1),
strides=(2, 2),
padding='same',
use_bias=False)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)
x = layers.SeparableConv2D(128, (3, 3),
padding='same',
use_bias=False,
name='block2_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv1_bn')(x)
x = layers.Activation('relu', name='block2_sepconv2_act')(x)
x = layers.SeparableConv2D(128, (3, 3),
padding='same',
use_bias=False,
name='block2_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv2_bn')(x)
x = layers.MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block2_pool')(x)
x = layers.add([x, residual])
residual = layers.Conv2D(256, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)
x = layers.Activation('relu', name='block3_sepconv1_act')(x)
x = layers.SeparableConv2D(256, (3, 3),
padding='same',
use_bias=False,
name='block3_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv1_bn')(x)
x = layers.Activation('relu', name='block3_sepconv2_act')(x)
x = layers.SeparableConv2D(256, (3, 3),
padding='same',
use_bias=False,
name='block3_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv2_bn')(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2),
padding='same',
name='block3_pool')(x)
x = layers.add([x, residual])
residual = layers.Conv2D(728, (1, 1),
strides=(2, 2),
padding='same',
use_bias=False)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)
x = layers.Activation('relu', name='block4_sepconv1_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block4_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv1_bn')(x)
x = layers.Activation('relu', name='block4_sepconv2_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block4_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv2_bn')(x)
x = layers.MaxPooling2D((3, 3), strides=(2, 2),
padding='same',
name='block4_pool')(x)
x = layers.add([x, residual])
for i in range(8):
residual = x
prefix = 'block' + str(i + 5)
x = layers.Activation('relu', name=prefix + '_sepconv1_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv1_bn')(x)
x = layers.Activation('relu', name=prefix + '_sepconv2_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv2_bn')(x)
x = layers.Activation('relu', name=prefix + '_sepconv3_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv3')(x)
x = layers.BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv3_bn')(x)
x = layers.add([x, residual])
residual = layers.Conv2D(1024, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)
x = layers.Activation('relu', name='block13_sepconv1_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block13_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block13_sepconv1_bn')(x)
x = layers.Activation('relu', name='block13_sepconv2_act')(x)
x = layers.SeparableConv2D(1024, (3, 3),
padding='same',
use_bias=False,
name='block13_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block13_sepconv2_bn')(x)
x = layers.MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block13_pool')(x)
x = layers.add([x, residual])
x = layers.SeparableConv2D(1536, (3, 3),
padding='same',
use_bias=False,
name='block14_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block14_sepconv1_bn')(x)
x = layers.Activation('relu', name='block14_sepconv1_act')(x)
x = layers.SeparableConv2D(2048, (3, 3),
padding='same',
use_bias=False,
name='block14_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block14_sepconv2_bn')(x)
x = layers.Activation('relu', name='block14_sepconv2_act')(x)
x=cbam(x)
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = layers.Dense(classes, activation='softmax', name='predictions')(x)
# Create model.
model = Model(img_input, x, name='xception')
return model
model = Xception()
model.summary()
#tf.keras.utils.plot_model(model)
model.compile(loss="categorical_crossentropy",
optimizer="sgd", metrics=['accuracy'])
epochs = 10
history = model.fit_generator(train_generator,
steps_per_epoch=train_num // batch_size,
epochs=epochs,
validation_data=valid_generator,
validation_steps=valid_num // batch_size)
def plot_learning_curves(history, label, epcohs, min_value, max_value):
data = {}
data[label] = history.history[label]
data['val_' + label] = history.history['val_' + label]
pd.DataFrame(data).plot(figsize=(8, 5))
plt.grid(True)
plt.axis([0, epochs, min_value, max_value])
plt.show()
plot_learning_curves(history, 'acc', epochs, 0, 1)
plot_learning_curves(history, 'loss', epochs, 0, 2)
来源：CSDN
作者：追梦小狂魔
链接：https://blog.csdn.net/qq_15557299/article/details/104339133