[Tensorflow][转载]monkeys数据集图像分类模型搭建训练

# To add a new cell, type '# %%'

# To add a new markdown cell, type '# %% [markdown]'

# %%

from IPython import get_ipython

# %%

# This Python 3 environment comes with many helpful analytics libraries installed

# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python

# For example, here's several helpful packages to load in 

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the "../input/" directory.

# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

import os

print(os.listdir("../input"))

# Any results you write to the current directory are saved as output.

# %%

get_ipython().run_line_magic('matplotlib', 'inline')

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

print(tf.__version__)

print(sys.version_info)

for module in mpl, np, pd, sklearn, tf, keras:

    print(module.__name__, module.__version__)

# %%

train_dir = "../input/training/training"

valid_dir = "../input/validation/validation"

label_file = "../input/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 = 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)

# %%

for i in range(2):

    x, y = train_generator.next()

    print(x.shape, y.shape)

    print(y)

# %%

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),

    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()

# %%

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, 1.5, 2.5)