tensorflow keras 学习笔记-优快云博客

本文链接：https://blog.youkuaiyun.com/ResumeProject/article/details/124695144

之前都是用pytorch 平台，现在看一下tensorflow的代码，并做了以下笔记代码大多复制于此链接

网络结构参考代码链接

# tf.keras.models.Sequential()

"""
model = keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape=[28, 28]))
model.add(keras.layers.Dense(300, activation="relu"))
model.add(keras.layers.Dense(100, activation="relu"))
model.add(keras.layers.Dense(10, activation="softmax"))
"""

model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=[28, 28]),
    keras.layers.Dense(300, activation='relu'),
    keras.layers.Dense(100, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

# relu: y = max(0, x)
# softmax: 将向量变成概率分布. x = [x1, x2, x3], 
#          y = [e^x1/sum, e^x2/sum, e^x3/sum], sum = e^x1 + e^x2 + e^x3

# reason for sparse: y->index. y->one_hot->[] 
model.compile(loss="sparse_categorical_crossentropy",
              optimizer = "sgd",
              metrics = ["accuracy"])

summary 功能：model.summary()torch需要安装 pytorch-summary

训练(参考代码链接)

model.compile(loss="mean_squared_error", optimizer="sgd")
callbacks = [keras.callbacks.EarlyStopping(
    patience=5, min_delta=1e-2)]
# 可以设置早停的callbacks
history = model.fit(x_train_scaled, y_train,
                    validation_data = (x_valid_scaled, y_valid),
                    epochs = 100,
                    callbacks = callbacks)

卷积神经网络结构参考代码链接

model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=32, kernel_size=3,
                              padding='same',
                              activation='relu',
                              input_shape=(28, 28, 1)))
model.add(keras.layers.Conv2D(filters=32, kernel_size=3,
                              padding='same',
                              activation='relu'))
model.add(keras.layers.MaxPool2D(pool_size=2))
model.add(keras.layers.Conv2D(filters=64, kernel_size=3,
                              padding='same',
                              activation='relu'))
model.add(keras.layers.Conv2D(filters=64, kernel_size=3,
                              padding='same',
                              activation='relu'))
model.add(keras.layers.MaxPool2D(pool_size=2))
model.add(keras.layers.Conv2D(filters=128, kernel_size=3,
                              padding='same',
                              activation='relu'))
model.add(keras.layers.Conv2D(filters=128, kernel_size=3,
                              padding='same',
                              activation='relu'))
model.add(keras.layers.MaxPool2D(pool_size=2))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dense(10, activation="softmax"))

model.compile(loss="sparse_categorical_crossentropy",
              optimizer = "sgd",
              metrics = ["accuracy"])

构建自己的数据集参考代码链接

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_dataframe(
    train_df,
    directory = './',
    x_col = 'filepath',
    y_col = 'class',
    classes = class_names,
    target_size = (height, width),
    batch_size = batch_size,
    seed = 7,
    shuffle = True,
    class_mode = 'sparse',
)

valid_datagen = keras.preprocessing.image.ImageDataGenerator(
    rescale = 1./255)
valid_generator = valid_datagen.flow_from_dataframe(
    valid_df,
    directory = './',
    x_col = 'filepath',
    y_col = 'class',
    classes = class_names,
    target_size = (height, width),
    batch_size = batch_size,
    seed = 7,
    shuffle = False,
    class_mode = "sparse")

train_num = train_generator.samples
valid_num = valid_generator.samples

网络结构的自动调整（使用Keras Tuner自动调参参考 参考）

def build_model(hp):
  inputs = tf.keras.Input(shape=(32, 32, 3))
  x = inputs
  for i in range(hp.Int('conv_blocks', 3, 5, default=3)):
    filters = hp.Int('filters_' + str(i), 32, 256, step=32)
    for _ in range(2):
      x = tf.keras.layers.Convolution2D(
        filters, kernel_size=(3, 3), padding='same')(x)
      x = tf.keras.layers.BatchNormalization()(x)
      x = tf.keras.layers.ReLU()(x)
    if hp.Choice('pooling_' + str(i), ['avg', 'max']) == 'max':
      x = tf.keras.layers.MaxPool2D()(x)
    else:
      x = tf.keras.layers.AvgPool2D()(x)
  x = tf.keras.layers.GlobalAvgPool2D()(x)
  x = tf.keras.layers.Dense(
      hp.Int('hidden_size', 30, 100, step=10, default=50),
      activation='relu')(x)
  x = tf.keras.layers.Dropout(
      hp.Float('dropout', 0, 0.5, step=0.1, default=0.5))(x)
  outputs = tf.keras.layers.Dense(10, activation='softmax')(x)

  model = tf.keras.Model(inputs, outputs)
  model.compile(
    optimizer=tf.keras.optimizers.Adam(
      hp.Float('learning_rate', 1e-4, 1e-2, sampling='log')),
    loss='sparse_categorical_crossentropy', 
    metrics=['accuracy'])
  return model