從零基礎入門Tensorflow2.0 ----六、30 kaggle 10 monkeys 基礎模型搭建與訓練

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0. 前言

10 monkeys 基礎模型搭建與訓練

1. 代碼部分

1. 導入模塊

%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
import os,sys
import tensorflow as tf
import time
from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl,pd,sklearn,tf,keras:
    print(module.__name__,module.__version__)

2. 讀取數據

# 文件路徑
train_dir = '../input/10-monkey-species/training/training'
valid_dir = "../input/10-monkey-species/validation/validation"
label_file = '../input/10-monkey-species/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))

# 讀取數據
labels = pd.read_csv(label_file,header=0)
print(labels)

3. 讀取圖片

# 讀取圖片
height = 128
width = 128
channels = 3
batch_size = 64
num_classes = 10

train_datagen = keras.preprocessing.image.ImageDataGenerator(
rescale = 1. / 255,
    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(rescale=1./255)
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)

# 讀取數據
for i in range(2):
    x,y = train_generator.next()
    print(x.shape,y.shape)
    print(y)

4. 構建模型

model = keras.models.Sequential([
    keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation='relu',input_shape=[width,height,channels]),
    keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation='relu'),
    keras.layers.MaxPool2D(pool_size=2),
    
    keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'),
    keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'),
    keras.layers.MaxPool2D(pool_size=2),
    
    keras.layers.Conv2D(filters=128,kernel_size=3,padding='same',activation='relu'),
    keras.layers.Conv2D(filters=128,kernel_size=3,padding='same',activation='relu'),
    keras.layers.MaxPool2D(pool_size=2),
    
    # 展平
    keras.layers.Flatten(),
    keras.layers.Dense(128,activation='relu'),
    keras.layers.Dense(num_classes,activation='softmax'),
])

model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()

5. 訓練

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)

print(history.history.keys())

6. 學習曲線

# 學習曲線
def plot_learning_curves(hsitory,label,epochs,min_value,max_value):
    data = {}
    data[label] = history.history[label]
    data['val_' + label] = hsitory.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,'accuracy',epochs,0,1)
plot_learning_curves(history,'loss',epochs,0,2.5)