finalDemo.py

import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ReduceLROnPlateau

np.random.seed(2)
train = pd.read_csv("./input/train.csv")
test = pd.read_csv("./input/test.csv")

Y_train = train["label"]  # 获得label
X_train = train.drop(labels=["label"], axis=1)  # 获得label以外的东西，也即是数据
del train   # 没用了

X_train.isnull().any().describe()   # isnull是所有空数据，any是进行与运算，describe其实是用来查看第一个数据是什么的

test.isnull().any().describe()

X_train = X_train / 255.0
test = test / 255.0
X_train = X_train.values.reshape(-1, 28, 28, 1)   # 这里就是按照28*28的图片大小进行压缩
test = test.values.reshape(-1, 28, 28, 1)      # 这个地方的这种写法，能够成功地将图片转化成28*28 * N的格式


Y_train = to_categorical(Y_train, num_classes=10)  # onhot

random_seed = 2
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size=0.1, random_state=random_seed)

model = Sequential()

model.add(Conv2D(filters=32, kernel_size=(5, 5), padding='Same', activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(filters=32, kernel_size=(5, 5), padding='Same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='Same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation="softmax"))
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])

learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', patience=3, verbose=1, factor=0.5, min_lr=0.00001)
epochs = 50  # 训练次数
batch_size = 256
datagen = ImageDataGenerator(
    featurewise_center=False,  # set input mean to 0 over the dataset
    samplewise_center=False,  # set each sample mean to 0
    featurewise_std_normalization=False,  # divide inputs by std of the dataset
    samplewise_std_normalization=False,  # divide each input by its std
    zca_whitening=False,  # apply ZCA whitening
    rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
    zoom_range=0.1,  # Randomly zoom image
    width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
    height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
    horizontal_flip=False,  # randomly flip images
    vertical_flip=False)  # randomly flip images

datagen.fit(X_train)
# 开始训练
history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
                              epochs=epochs, validation_data=(X_val, Y_val),
                              verbose=2, steps_per_epoch=X_train.shape[0] // batch_size
                              , callbacks=[learning_rate_reduction])


results = model.predict(test)
results = np.argmax(results, axis=1)
results = pd.Series(results, name="Label")
submission = pd.concat([pd.Series(range(1, 28001), name="ImageId"), results], axis=1)
submission.to_csv('./output/wyh_result.csv', index=False)