Add save/load comments in keras docs (#3820)
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1 changed files with 134 additions and 92 deletions
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@ -6,9 +6,9 @@
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To define a model in Scala using the Keras-like API, one just needs to import the following packages:
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers._
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import com.intel.analytics.zoo.pipeline.api.keras.models._
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers._
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import com.intel.analytics.bigdl.dllib.keras.models._
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import com.intel.analytics.bigdl.dllib.utils.Shape
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```
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One of the highlighted features with regard to the new API is __shape inference__. Users only need to specify the input shape (a `Shape` object __excluding__ batch dimension, for example, `inputShape=Shape(3, 4)` for 3D input) for the first layer of a model and for the remaining layers, the input dimension will be automatically inferred.
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@ -19,9 +19,9 @@ Here we use the Keras-like API to define a LeNet CNN model and train it on the M
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```scala
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import com.intel.analytics.bigdl.numeric.NumericFloat
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import com.intel.analytics.zoo.pipeline.api.keras.layers._
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import com.intel.analytics.zoo.pipeline.api.keras.models._
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers._
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import com.intel.analytics.bigdl.dllib.keras.models._
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import com.intel.analytics.bigdl.dllib.utils.Shape
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val model = Sequential()
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model.add(Reshape(Array(1, 28, 28), inputShape = Shape(28, 28, 1)))
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@ -81,9 +81,9 @@ Sequential()
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Example code to create a sequential model:
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.{Dense, Activation}
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.{Dense, Activation}
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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val model = Sequential[Float]()
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model.add(Dense[Float](32, inputShape = Shape(128)))
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@ -114,7 +114,7 @@ Parameters:
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To merge a list of input __nodes__ (__NOT__ layers), following some merge mode in the Functional API:
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Merge.merge
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import com.intel.analytics.bigdl.dllib.keras.layers.Merge.merge
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merge(inputs, mode = "sum", concatAxis = -1) // This will return an output NODE.
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```
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@ -127,10 +127,10 @@ Parameters:
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Example code to create a graph model:
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.{Dense, Input}
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Merge.merge
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import com.intel.analytics.zoo.pipeline.api.keras.models.Model
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.{Dense, Input}
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import com.intel.analytics.bigdl.dllib.keras.layers.Merge.merge
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import com.intel.analytics.bigdl.dllib.keras.models.Model
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import com.intel.analytics.bigdl.dllib.utils.Shape
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// instantiate input nodes
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val input1 = Input[Float](inputShape = Shape(8))
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@ -169,9 +169,9 @@ Masking(mask_value=0.0, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Masking
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.Masking
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -252,8 +252,8 @@ SparseDense(output_dim, init="glorot_uniform", activation=None, W_regularizer=No
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.SparseDense
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.SparseDense
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val layer = SparseDense[Float](outputDim = 5, inputShape = Shape(2, 4))
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@ -340,9 +340,9 @@ SoftShrink(value = 0.5, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.SoftShrink
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.SoftShrink
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -474,9 +474,9 @@ Reshape(target_shape, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Reshape
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.Reshape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -587,9 +587,9 @@ Merge(layers=None, mode="sum", concat_axis=-1, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.InputLayer
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Merge
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.InputLayer
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import com.intel.analytics.bigdl.dllib.keras.layers.Merge
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.{Shape, T}
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import com.intel.analytics.bigdl.tensor.Tensor
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@ -711,9 +711,9 @@ MaxoutDense(output_dim, nb_feature=4, W_regularizer=None, b_regularizer=None, bi
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.MaxoutDense
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.MaxoutDense
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -784,9 +784,9 @@ Squeeze(dim=None, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Squeeze
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.Squeeze
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -865,9 +865,9 @@ BinaryThreshold(value=1e-6, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.BinaryThreshold
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.BinaryThreshold
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -995,9 +995,9 @@ Sqrt(input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Sqrt
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.Sqrt
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1063,9 +1063,9 @@ Mul(input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Mul
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.Mul
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1156,9 +1156,9 @@ MulConstant(constant, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.MulConstant
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.MulConstant
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1253,9 +1253,9 @@ Scale(size, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Scale
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.Scale
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1320,9 +1320,9 @@ Log(input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Log
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.Log
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1422,9 +1422,9 @@ Identity(input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Identity
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.layers.Identity
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1557,8 +1557,8 @@ Select(dim, index, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Select
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.Select
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1634,9 +1634,9 @@ Dense(output_dim, init="glorot_uniform", activation=None, W_regularizer=None, b_
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Dense
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.Dense
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1701,9 +1701,9 @@ Negative(input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Negative
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.Negative
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1789,9 +1789,9 @@ CAdd(size, b_regularizer=None, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.CAdd
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.CAdd
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1872,9 +1872,9 @@ RepeatVector(n, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.RepeatVector
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.RepeatVector
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -1954,8 +1954,8 @@ GaussianSampler(input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.GaussianSampler
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.GaussianSampler
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import com.intel.analytics.bigdl.utils.{Shape, MultiShape, T}
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import com.intel.analytics.bigdl.tensor.Tensor
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@ -2054,9 +2054,9 @@ Exp(input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Exp
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.Exp
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -2186,9 +2186,9 @@ Square(input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Square
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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import com.intel.analytics.bigdl.dllib.keras.layers.Square
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import com.intel.analytics.bigdl.dllib.utils.Shape
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import com.intel.analytics.bigdl.tensor.Tensor
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val model = Sequential[Float]()
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@ -2320,9 +2320,9 @@ Power(power, scale=1, shift=0, input_shape=None, name=None)
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**Scala example:**
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```scala
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import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
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import com.intel.analytics.zoo.pipeline.api.keras.layers.Power
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import com.intel.analytics.bigdl.utils.Shape
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import com.intel.analytics.bigdl.dllib.keras.models.Sequential
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||||
import com.intel.analytics.bigdl.dllib.keras.layers.Power
|
||||
import com.intel.analytics.bigdl.dllib.utils.Shape
|
||||
import com.intel.analytics.bigdl.tensor.Tensor
|
||||
|
||||
val model = Sequential[Float]()
|
||||
|
|
@ -2404,9 +2404,9 @@ AddConstant(constant, input_shape=None, name=None)
|
|||
|
||||
**Scala example:**
|
||||
```scala
|
||||
import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
|
||||
import com.intel.analytics.zoo.pipeline.api.keras.layers.AddConstant
|
||||
import com.intel.analytics.bigdl.utils.Shape
|
||||
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
|
||||
import com.intel.analytics.bigdl.dllib.keras.layers.AddConstant
|
||||
import com.intel.analytics.bigdl.dllib.utils.Shape
|
||||
import com.intel.analytics.bigdl.tensor.Tensor
|
||||
|
||||
val model = Sequential[Float]()
|
||||
|
|
@ -2509,9 +2509,9 @@ Narrow(dim, offset, length=1, input_shape=None, name=None)
|
|||
|
||||
**Scala example:**
|
||||
```scala
|
||||
import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
|
||||
import com.intel.analytics.zoo.pipeline.api.keras.layers.Narrow
|
||||
import com.intel.analytics.bigdl.utils.Shape
|
||||
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
|
||||
import com.intel.analytics.bigdl.dllib.keras.layers.Narrow
|
||||
import com.intel.analytics.bigdl.dllib.utils.Shape
|
||||
import com.intel.analytics.bigdl.tensor.Tensor
|
||||
|
||||
val model = Sequential[Float]()
|
||||
|
|
@ -2622,9 +2622,9 @@ Permute(dims, input_shape=None, name=None)
|
|||
|
||||
**Scala example:**
|
||||
```scala
|
||||
import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
|
||||
import com.intel.analytics.zoo.pipeline.api.keras.layers.Permute
|
||||
import com.intel.analytics.bigdl.utils.Shape
|
||||
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
|
||||
import com.intel.analytics.bigdl.dllib.keras.layers.Permute
|
||||
import com.intel.analytics.bigdl.dllib.utils.Shape
|
||||
import com.intel.analytics.bigdl.tensor.Tensor
|
||||
|
||||
val model = Sequential[Float]()
|
||||
|
|
@ -2729,9 +2729,9 @@ ResizeBilinear(output_height, output_width, align_corner=False, dim_ordering="th
|
|||
|
||||
**Scala example:**
|
||||
```scala
|
||||
import com.intel.analytics.zoo.pipeline.api.keras.models.Sequential
|
||||
import com.intel.analytics.zoo.pipeline.api.keras.layers.ResizeBilinear
|
||||
import com.intel.analytics.bigdl.utils.Shape
|
||||
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
|
||||
import com.intel.analytics.bigdl.dllib.keras.layers.ResizeBilinear
|
||||
import com.intel.analytics.bigdl.dllib.utils.Shape
|
||||
import com.intel.analytics.bigdl.tensor.Tensor
|
||||
|
||||
val model = Sequential()
|
||||
|
|
@ -2831,3 +2831,45 @@ array([[[[0.43790358, 0.61913717, 0.2543214 ],
|
|||
[[0.12074634, 0.6571231 , 0.752728 ],
|
||||
[0.86969995, 0.6700518 , 0.36353552]]]], dtype=float32)
|
||||
```
|
||||
---
|
||||
## 8. Persistence
|
||||
This section describes how to save and load the Keras-like API.
|
||||
|
||||
### 8.1 save
|
||||
To save a Keras model, you call the method `saveModel(path)`.
|
||||
|
||||
**Scala:**
|
||||
```scala
|
||||
import com.intel.analytics.bigdl.dllib.keras.layers.{Dense, Activation}
|
||||
import com.intel.analytics.bigdl.dllib.keras.models.Sequential
|
||||
|
||||
val model = Sequential[Float]()
|
||||
model.add(Dense[Float](32, inputShape = Shape(128)))
|
||||
model.add(Activation[Float]("relu"))
|
||||
model.saveModel("/tmp/seq.model")
|
||||
```
|
||||
**Python:**
|
||||
```python
|
||||
import bigdl.dllib.keras.Sequential
|
||||
from bigdl.dllib.keras.layer import Dense
|
||||
|
||||
model = Sequential()
|
||||
model.add(Dense(input_shape=(32, )))
|
||||
model.saveModel("/tmp/seq.model")
|
||||
```
|
||||
|
||||
### 8.2 load
|
||||
To load a saved Keras model, you call the method `load_model(path)`.
|
||||
|
||||
**Scala:**
|
||||
```scala
|
||||
import com.intel.analytics.bigdl.dllib.keras.Models
|
||||
|
||||
val model = Models.loadModel[Float]("/tmp/seq.model")
|
||||
```
|
||||
|
||||
**Python:**
|
||||
```python
|
||||
from bigdl.dllib.keras.models
|
||||
model = load_model("/tmp/seq.model")
|
||||
```
|
||||
|
|
|
|||
Loading…
Reference in a new issue