import bmnett ## compile fp32 model bmnett.compile( model = "AnimeGAN_dynamic.pb", ## Necessary outdir = "./compilation1684", ## Necessary target = "BM1684", ## Necessary shapes = [[1,700,1024,3]], ## Necessary net_name = "animate", ## Necessary input_names=["test"], ## Necessary, when .h5 use None output_names=["generator/G_MODEL/Tanh"], ## Necessary, when .h5 use None opt = 2, ## optional, if not set, default equal to 1 dyn = False, ## optional, if not set, default equal to False cmp = False, ## optional, if not set, default equal to True enable_profile = False ## optional, if not set, default equal to False )
时间: 2024-02-14 20:36:12 浏览: 9
这段代码使用了 `bmnett` 库来编译一个模型。它首先导入了 `bmnett` 模块,然后调用了 `bmnett.compile()` 函数来编译模型。
在函数的参数中,它指定了要编译的模型文件名为 `"AnimeGAN_dynamic.pb"`,输出目录为 `"./compilation1684"`,目标设备为 `"BM1684"`,输入数据的形状为 `[[1,700,1024,3]]`,模型名称为 `"animate"`,输入名称为 `"test"`,输出名称为 `"generator/G_MODEL/Tanh"`。此外,它还设置了一些可选的参数,如 `opt`、`dyn`、`cmp` 和 `enable_profile`。
具体而言,`opt` 参数指定了优化级别,值为 `2` 表示使用先进的优化策略;`dyn` 参数指定是否启用动态形状,值为 `False` 表示不启用;`cmp` 参数指定是否启用压缩,值为 `False` 表示不启用;`enable_profile` 参数指定是否启用性能分析,值为 `False` 表示不启用。
如果你有任何问题或需要更多信息,请提供更多上下文和详细信息。
相关问题
# 最佳权重保存路径 BEST_MODEL_PATH = './best_model.h5'
好的,你可以将最佳权重保存路径添加到代码中,如下所示:
```python
import numpy as np
import tensorflow as tf
import os
# 加载数据集
with open('poems.txt', 'r', encoding='utf-8') as f:
data = f.read()
# 构建词典
vocab = sorted(set(data))
char2idx = {char: idx for idx, char in enumerate(vocab)}
idx2char = np.array(vocab)
# 将文本数据转换为数字
text_as_int = np.array([char2idx[c] for c in data])
# 定义训练数据和标签
seq_length = 100
examples_per_epoch = len(data) // (seq_length + 1)
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
sequences = char_dataset.batch(seq_length + 1, drop_remainder=True)
def split_input_target(chunk):
input_text = chunk[:-1]
target_text = chunk[1:]
return input_text, target_text
dataset = sequences.map(split_input_target)
BATCH_SIZE = 128
BUFFER_SIZE = 10000
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
# 构建模型
vocab_size = len(vocab)
embedding_dim = 256
rnn_units = 1024
def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim,
batch_input_shape=[batch_size, None]),
tf.keras.layers.GRU(rnn_units,
return_sequences=True,
stateful=True,
recurrent_initializer='glorot_uniform'),
tf.keras.layers.Dense(vocab_size)
])
return model
model = build_model(
vocab_size=len(vocab),
embedding_dim=embedding_dim,
rnn_units=rnn_units,
batch_size=BATCH_SIZE)
# 定义损失函数
def loss(labels, logits):
return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)
# 编译模型
model.compile(optimizer='adam', loss=loss)
# 定义检查点
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_prefix,
save_weights_only=True)
# 定义最佳权重检查点
BEST_MODEL_PATH = './best_model.h5'
best_checkpoint = tf.keras.callbacks.ModelCheckpoint(BEST_MODEL_PATH,
monitor='val_loss',
save_best_only=True,
mode='min',
save_weights_only=True)
# 训练模型
EPOCHS = 50
history = model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback, best_checkpoint])
# 生成诗歌
def generate_text(model, start_string):
num_generate = 100
input_eval = [char2idx[s] for s in start_string]
input_eval = tf.expand_dims(input_eval, 0)
text_generated = []
temperature = 1.0
model.reset_states()
for i in range(num_generate):
predictions = model(input_eval)
predictions = tf.squeeze(predictions, 0)
predictions = predictions / temperature
predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
input_eval = tf.expand_dims([predicted_id], 0)
text_generated.append(idx2char[predicted_id])
return (start_string + ''.join(text_generated))
# 加载检查点
model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)
model.load_weights(BEST_MODEL_PATH)
model.build(tf.TensorShape([1, None]))
# 生成一首诗
print(generate_text(model, start_string=u"山中"))
```
现在,模型将保存最佳的权重到文件 `best_model.h5`。
伪代码#train checkpoint_path = './model_pth/cnn.h5' checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, monitor='val_accuracy', verbose=1,save_best_only=True, mode='max') early_stopping = tf.keras.callbacks.EarlyStopping(monitor ='val_loss', patience=200, verbose=1) optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4, epsilon=1e-7) # compile the keras model model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) callbacks_list = [checkpoint, early_stopping] # fit the keras model on the dataset start = time.time() history = model.fit(X_train, y_train, validation_split = 0.1, epochs=1000, batch_size=32, verbose=2,callbacks = callbacks_list) end = time.time() print('模型训练时间: ',end - start)
以下是伪代码的解释:
```
# 导入必要的库
import tensorflow as tf
import time
# 定义模型的保存路径并设置 ModelCheckpoint 回调函数
checkpoint_path = './model_pth/cnn.h5'
checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
# 设置 EarlyStopping 回调函数
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=200, verbose=1)
# 设置 Adam 优化器
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4, epsilon=1e-7)
# 编译 Keras 模型
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
# 设置回调函数列表
callbacks_list = [checkpoint, early_stopping]
# 训练 Keras 模型
start = time.time()
history = model.fit(X_train, y_train, validation_split=0.1, epochs=1000, batch_size=32, verbose=2, callbacks=callbacks_list)
end = time.time()
# 打印模型训练时间
print('模型训练时间: ', end - start)
```
该伪代码主要是使用 TensorFlow 的 Keras API 来训练一个卷积神经网络模型。在训练过程中,使用了 ModelCheckpoint 回调函数来保存最佳模型,EarlyStopping 回调函数来在模型出现过拟合时停止训练,以及 Adam 优化器来优化模型的学习过程。同时该模型在训练时使用了交叉验证的方法来验证模型的性能,训练的过程中也使用了批量梯度下降的方法来优化模型的训练速度。最后打印出模型训练时间。