Python load_data示例

示例#1

import tensorflow as tf
from tensorflow.keras.layers import *
import numpy as np
import img_loader

x_train, y_train = img_loader.load_data("../flower_photos")
x_train = x_train / 255.0

model = tf.keras.Sequential()
model.add(Conv2D(100, kernel_size=(5,5), activation='relu', input_shape=(224,224,3)))
model.add(MaxPooling2D(pool_size=(3,3)))

model.add(Conv2D(100, kernel_size=(4,4), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(5, activation='softmax'))

model.compile(optimizer='adam',
              loss=tf.keras.losses.CategoricalCrossentropy(),
              metrics=['accuracy'])

model.fit(x=x_train, y=y_train, batch_size=50, epochs=20)
model.save("model3.h5")

示例#2

文件： model3.py 项目： undefined-None/Dogs-vs-Cats-Image-classifier-with-keras-and-saved-trained-models

import tensorflow as tf
from tensorflow.keras.layers import *
import numpy as np
import img_loader

x_train, y_train = img_loader.load_data("../dogs-vs-cats/train")
x_train = x_train / 255.0

model = tf.keras.Sequential()
model.add(
    Conv2D(32,
           kernel_size=(3, 3),
           activation='relu',
           input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

model.compile(optimizer='rmsprop',
              loss=tf.keras.losses.CategoricalCrossentropy(),
              metrics=['accuracy'])