STT-tensorflow/tensorflow/tools/api/golden/v1/tensorflow.layers.-flatten.pbtxt
Reed Wanderman-Milne f22e833a2b Initial support for mixed precision policies.
A tf.keras.mixed_precision.experimental.Policy determines the dtype of layer computations and variables. In this initial implementation, policies only determine layer variable dtypes, and support for determining layer computation dtypes will come later.

Co-authored-by: James Qin <jamesqin@google.com>
PiperOrigin-RevId: 236010048
2019-02-27 17:13:33 -08:00

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path: "tensorflow.layers.Flatten"
tf_class {
is_instance: "<class \'tensorflow.python.layers.core.Flatten\'>"
is_instance: "<class \'tensorflow.python.keras.layers.core.Flatten\'>"
is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
is_instance: "<type \'object\'>"
member {
name: "activity_regularizer"
mtype: "<type \'property\'>"
}
member {
name: "dtype"
mtype: "<type \'property\'>"
}
member {
name: "dynamic"
mtype: "<type \'property\'>"
}
member {
name: "graph"
mtype: "<type \'property\'>"
}
member {
name: "inbound_nodes"
mtype: "<type \'property\'>"
}
member {
name: "input"
mtype: "<type \'property\'>"
}
member {
name: "input_mask"
mtype: "<type \'property\'>"
}
member {
name: "input_shape"
mtype: "<type \'property\'>"
}
member {
name: "losses"
mtype: "<type \'property\'>"
}
member {
name: "name"
mtype: "<type \'property\'>"
}
member {
name: "non_trainable_variables"
mtype: "<type \'property\'>"
}
member {
name: "non_trainable_weights"
mtype: "<type \'property\'>"
}
member {
name: "outbound_nodes"
mtype: "<type \'property\'>"
}
member {
name: "output"
mtype: "<type \'property\'>"
}
member {
name: "output_mask"
mtype: "<type \'property\'>"
}
member {
name: "output_shape"
mtype: "<type \'property\'>"
}
member {
name: "scope_name"
mtype: "<type \'property\'>"
}
member {
name: "trainable_variables"
mtype: "<type \'property\'>"
}
member {
name: "trainable_weights"
mtype: "<type \'property\'>"
}
member {
name: "updates"
mtype: "<type \'property\'>"
}
member {
name: "variables"
mtype: "<type \'property\'>"
}
member {
name: "weights"
mtype: "<type \'property\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
}
member_method {
name: "add_loss"
argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'aggregation\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_variable"
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "add_weight"
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'use_resource\', \'synchronization\', \'aggregation\', \'partitioner\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\', \'None\'], "
}
member_method {
name: "apply"
argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "build"
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "call"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "compute_mask"
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "compute_output_shape"
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "count_params"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "from_config"
argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_config"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_mask_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_shape_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_losses_for"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_mask_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_shape_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_updates_for"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_weights"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "set_weights"
argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
}
}