tensorflow学习笔记（一）

前言

刚刚学习tensorflow，使用还不太熟练，这里引用几个模型的案例程序，以及保存的各种格式。

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话不多说，直接上代码

一、mnist

# import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras

print(tf.version.VERSION)

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

train_labels = train_labels[:1000]
test_labels = test_labels[:1000]

# plt.imshow(train_images[0])
# plt.show()

train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0
test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0

def create_model():
    model = tf.keras.models.Sequential([
        keras.layers.Dense(512, activation='relu', input_shape=(784,)),
        keras.layers.Dropout(0.2),
        keras.layers.Dense(10, activation='softmax')
    ])

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

    return model

model = create_model()

model.summary()

model.fit(train_images,
          train_labels,
          epochs=5,
          )

model.save('my_model.h5')

loss, acc = model.evaluate(test_images, test_labels)
print(" model, accuracy: {:5.2f}%".format(100 * acc))

二、CIFAR10

import tensorflow as tf
from tensorflow.keras import datasets,layers,optimizers,Sequential,metrics
from tensorflow import keras
import os

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

def preprocess(x,y):
    x=2*tf.cast(x,dtype=tf.float32)/255 - 1
    y=tf.cast(y,dtype=tf.int32)
    return x,y

batchsz=128
(x,y),(x_test,y_test)=datasets.cifar10.load_data()
y=tf.squeeze(y)
y_test=tf.squeeze(y_test)
y=tf.one_hot(y,depth=10)
y_test=tf.one_hot(y_test,depth=10)
print('datasets:',x.shape,y.shape,x.min(),x.max())

train_db=tf.data.Dataset.from_tensor_slices((x,y))
train_db=train_db.map(preprocess).shuffle(10000).batch(batchsz)
test_db=tf.data.Dataset.from_tensor_slices((x_test,y_test))
test_db=test_db.map(preprocess).batch(batchsz)

sample=next(iter(train_db))
print(sample[0].shape,sample[1].shape)

class MyDense(layers.Layer):
    # to replave standaed layers.Dense
    def __init__(self,inp_dim,outp_dim):
        super(MyDense, self).__init__()

        self.kernel=self.add_variable('w',[inp_dim,outp_dim])
        # self.bias=self.add_variable('b',[outp_dim])

    def call(self, inputs, training=None):

        x=inputs@self.kernel
        return x

class MyNetwork(keras.Model):
    def __init__(self):
        super(MyNetwork, self).__init__()

        self.fc1=MyDense(32*32*3,256)
        self.fc2=MyDense(256,128)
        self.fc3=MyDense(128,64)
        self.fc4=MyDense(64,32)
        self.fc5=MyDense(32,10)

    def call(self, inputs, training=None):
        # pass
        x = tf.reshape(inputs,[-1,32*32*3])
        x = self.fc1(x)
        x = tf.nn.relu(x)
        x = self.fc2(x)
        x = tf.nn.relu(x)
        x = self.fc3(x)
        x = tf.nn.relu(x)
        x = self.fc4(x)
        x = tf.nn.relu(x)
        x = self.fc5(x)

        return x


network=MyNetwork()
network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
network.fit(train_db,epochs=15,validation_data=test_db,validation_freq=1)

network.evaluate(test_db)
network.save('ckpt/weights.h5')
del network
print('xiaoan.ckpt')

network=MyNetwork()

network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
network.load_weights('xiaoan.ckpt')
print('loaded weights from file.')
network.evaluate(test_db)

三、fashon_mnist

代码如下：

import tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics

import  os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

def preprocess(x, y):

    x = tf.cast(x, dtype=tf.float32) / 255.
    y = tf.cast(y, dtype=tf.int32)
    return x,y


(x, y), (x_test, y_test) = datasets.fashion_mnist.load_data()
print(x.shape, y.shape)

batchsz = 128

db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(10000).batch(batchsz)

db_test = tf.data.Dataset.from_tensor_slices((x_test,y_test))
db_test = db_test.map(preprocess).batch(batchsz)

db_iter = iter(db)
sample = next(db_iter)
print('batch:', sample[0].shape, sample[1].shape)


model = Sequential([
    layers.Dense(256, activation=tf.nn.relu), # [b, 784] => [b, 256]
    layers.Dense(128, activation=tf.nn.relu), # [b, 256] => [b, 128]
    layers.Dense(64, activation=tf.nn.relu), # [b, 128] => [b, 64]
    layers.Dense(32, activation=tf.nn.relu), # [b, 64] => [b, 32]
    layers.Dense(10) # [b, 32] => [b, 10], 330 = 32*10 + 10
])
model.build(input_shape=[None, 28*28])
model.summary()
# w = w - lr*grad
optimizer = optimizers.Adam(lr=1e-3)

def main():


    for epoch in range(30):


        for step, (x,y) in enumerate(db):

            # x: [b, 28, 28] => [b, 784]
            # y: [b]
            x = tf.reshape(x, [-1, 28*28])

            with tf.GradientTape() as tape:
                # [b, 784] => [b, 10]
                logits = model(x)
                y_onehot = tf.one_hot(y, depth=10)
                # [b]
                loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot, logits))
                loss_ce = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True)
                loss_ce = tf.reduce_mean(loss_ce)

            grads = tape.gradient(loss_ce, model.trainable_variables)
            optimizer.apply_gradients(zip(grads, model.trainable_variables))


            if step % 100 == 0:
                print(epoch, step, 'loss:', float(loss_ce), float(loss_mse))


        # test
        total_correct = 0
        total_num = 0
        for x,y in db_test:

            # x: [b, 28, 28] => [b, 784]
            # y: [b]
            x = tf.reshape(x, [-1, 28*28])
            # [b, 10]
            logits = model(x)
            # logits => prob, [b, 10]
            prob = tf.nn.softmax(logits, axis=1)
            # [b, 10] => [b], int64
            pred = tf.argmax(prob, axis=1)
            pred = tf.cast(pred, dtype=tf.int32)
            # pred:[b]
            # y: [b]
            # correct: [b], True: equal, False: not equal
            correct = tf.equal(pred, y)
            correct = tf.reduce_sum(tf.cast(correct, dtype=tf.int32))

            total_correct += int(correct)
            total_num += x.shape[0]

        acc = total_correct / total_num
        print(epoch, 'test acc:', acc)







if __name__ == '__main__':
    main()