标签：layers Loss activation same padding 可视化 tf TF2 size
pip安装依赖pydot和graphviz并且安装软件sudo apt install graphviz，有个坑，windows安装软件之后安装的依赖是pydot-ng
注意：模型的第一层需要把形状传进去
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import datetime
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow.keras.utils import plot_model
conv_layers = [
layers.Conv2D(64, kernel_size=[3, 3], padding='same', activation=tf.nn.relu, input_shape=[32, 32, 3]),
layers.Conv2D(64, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Conv2D(128, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.Conv2D(128, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Conv2D(256, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.Conv2D(256, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
layers.Flatten(),
layers.Dense(256, activation=tf.nn.relu),
layers.Dense(128, activation=tf.nn.relu),
layers.Dense(100, activation=None),
]
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.cifar100.load_data()
# (50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)
y = tf.squeeze(y, axis=1)
y_test = tf.squeeze(y_test, axis=1)
train_db = tf.data.Dataset.from_tensor_slices((x, y))
train_db = train_db.shuffle(1000).map(preprocess).batch(64)
test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test))
test_db = test_db.map(preprocess).batch(64)
logdir = "logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
writer = tf.summary.create_file_writer(logdir=logdir)
def main():
model = Sequential(conv_layers)
model.summary()
plot_model(model=model, to_file="model.png", show_shapes=True, dpi=300)
variables = model.trainable_variables
optimizer = optimizers.Adam(lr=1e-4)
for epoch in range(10):
for step, (x, y) in enumerate(train_db):
with tf.GradientTape() as tape:
logits = model(x)
y_onehot = tf.one_hot(y, depth=100)
loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True)
loss = tf.reduce_mean(loss)
grads = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(grads, variables))
if step % 100 == 0:
print(epoch, step, 'loss', float(loss))
with writer.as_default():
tf.summary.scalar("train_loss", loss, epoch)
totol_num = 0
totol_correct = 0
for x, y in test_db:
logits = model(x)
prob = tf.nn.softmax(logits, axis=1)
pred = tf.argmax(prob, axis=1)
pred = tf.cast(pred, dtype=tf.int32)
correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
correct = tf.reduce_sum(correct)
totol_num += x.shape[0]
totol_correct += int(correct)
acc = totol_correct / totol_num
print(epoch, 'acc:', acc)
with writer.as_default():
tf.summary.scalar("val_acc", acc, epoch)
if __name__ == '__main__':
main()
标签：layers,Loss,activation,same,padding,可视化,tf,TF2,size 来源： https://www.cnblogs.com/consolexinhun/p/14292935.html
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