Sequential
將層的線性堆棧分組為 tf.keras.Model
。
用法
tf.keras.Sequential(
layers=None, name=None
)
參數
-
layers
要添加到模型的可選層列表。 -
name
模型的可選名稱。
屬性
-
distribute_strategy
該模型是在tf.distribute.Strategy
下創建的。 -
layers
-
metrics_names
返回所有輸出的模型顯示標簽。注意:
metrics_names
僅在keras.Model
已根據實際數據進行訓練/評估後可用。inputs = tf.keras.layers.Input(shape=(3,)) outputs = tf.keras.layers.Dense(2)(inputs) model = tf.keras.models.Model(inputs=inputs, outputs=outputs) model.compile(optimizer="Adam", loss="mse", metrics=["mae"]) model.metrics_names []
x = np.random.random((2, 3)) y = np.random.randint(0, 2, (2, 2)) model.fit(x, y) model.metrics_names ['loss', 'mae']
inputs = tf.keras.layers.Input(shape=(3,)) d = tf.keras.layers.Dense(2, name='out') output_1 = d(inputs) output_2 = d(inputs) model = tf.keras.models.Model( inputs=inputs, outputs=[output_1, output_2]) model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) model.fit(x, (y, y)) model.metrics_names ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc']
-
run_eagerly
指示模型是否應立即運行的可設置屬性。即刻地運行意味著您的模型將像 Python 代碼一樣逐步運行。您的模型可能會運行得更慢,但通過單步調用各個層調用,您應該可以更輕鬆地對其進行調試。
默認情況下,我們將嘗試將您的模型編譯為靜態圖以提供最佳執行性能。
Sequential
在此模型上提供訓練和推理函數。
例子:
# Optionally, the first layer can receive an `input_shape` argument:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
# Afterwards, we do automatic shape inference:
model.add(tf.keras.layers.Dense(4))
# This is identical to the following:
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(8))
# Note that you can also omit the `input_shape` argument.
# In that case the model doesn't have any weights until the first call
# to a training/evaluation method (since it isn't yet built):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(4))
# model.weights not created yet
# Whereas if you specify the input shape, the model gets built
# continuously as you are adding layers:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8, input_shape=(16,)))
model.add(tf.keras.layers.Dense(4))
len(model.weights)
# Returns "4"
# When using the delayed-build pattern (no input shape specified), you can
# choose to manually build your model by calling
# `build(batch_input_shape)`:
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(4))
model.build((None, 16))
len(model.weights)
# Returns "4"
# Note that when using the delayed-build pattern (no input shape specified),
# the model gets built the first time you call `fit`, `eval`, or `predict`,
# or the first time you call the model on some input data.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(8))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='sgd', loss='mse')
# This builds the model for the first time:
model.fit(x, y, batch_size=32, epochs=10)
相關用法
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注:本文由純淨天空篩選整理自tensorflow.org大神的英文原創作品 tf.keras.Sequential。非經特殊聲明,原始代碼版權歸原作者所有,本譯文未經允許或授權,請勿轉載或複製。