B һd;@sdZddlZddlZddlmZddlmZddlmZddlm Z ddlm Z dZ iZ d d Z d d ZGd ddZddZedddZedddZddZdS)z3TensorFlow Debugger: Tools for debugging gradients.N) debug_data) debug_graphs)ops) gen_array_ops) variablesZgradient_debug_cCs t|j\}}d||t|fS)Nz %s_%d/%s%s)rparse_node_or_tensor_namename_GRADIENT_DEBUG_TAG)tensorgrad_debugger_uuidop_nameZslotr ]/var/www/html/venv/lib/python3.7/site-packages/tensorflow/python/debug/lib/debug_gradients.py_tensor_to_grad_debug_op_namesrcCs|d}t|dkst|dts,t|dttd}d|krZ|d|d}t|d|dddd}|dd|dd}d|dd|gd|}||fS)aParse the name of a debug gradient op. Args: op_name: the name of the debug gradient op. Returns: 1) The UUID of the GradientsDebugger that created the debug gradient op. 2) Name of the original tensor whose gradient is debugged by the debug gradient op. /N_z:%d) splitlenAssertionError startswithr indexintrfindjoin)r Z name_itemsr Zorig_tensor_slotZorig_base_op_nameorig_tensor_namer r r_parse_grad_debug_op_name$s " rc@seZdZdZdddZeddZeddZd d Zd d Z d dZ ddZ ddZ ddZ ddZddZddZddZdS)GradientsDebuggerzGradients Debugger. Allows retrieval of gradient tensors created by TensorFlow's automatic differentiation algorithm, i.e., `tf.gradients` and optimizer classes that use it. NcCs>tj|_|t|j<i|_||_d|_|r4|j|_d|_ dS)zConstructor of GradientsDebugger. Args: y_tensor: optional: the `tf.Tensor` to be differentiated, i.e., the tensor on the numerator of the differentiation. NF) uuiduuid4hex_uuid_gradient_debuggers_gradient_tensors _y_tensor_graphgraph_is_active_context)selfy_tensorr r r__init__Gs  zGradientsDebugger.__init__cCs|jS)N)r&)r*r r rr+^szGradientsDebugger.y_tensorcCs|jS)N)r')r*r r rr(bszGradientsDebugger.graphcCs d|_dS)NT)r))r*r r r __enter__fszGradientsDebugger.__enter__cCs d|_dS)NF)r))r*Z unused_typeZ unused_valueZunused_tracebackr r r__exit__iszGradientsDebugger.__exit__cCsXt||j}|jjrtjntj}|||d}|j|jkss   95> rcCs tdS)z1Clear all globally registered gradient debuggers.N)r$clearr r r rclear_gradient_debuggersYsrWZDebugGradientIdentitycCs&t|j\}}t|}||||S)z+Gradient function for the DebugIdentity op.)rrr$rH)r/dyr r grad_debuggerr r r_identify_gradient_grad^s rZZDebugGradientRefIdentitycCs t||S)z+Gradient function for the DebugIdentity op.)rZ)r/rXr r r_identify_gradient_grad_refhsr[cCsp|jr,|jr,|j|jkr,td|j|jf||}t|j\}}y|||dStj k rjgSXdS)aFind gradient values from a `DebugDumpDir` object. Args: grad_debugger: the `tf_debug.GradientsDebugger` instance to be used. x_tensor: (`tf.Tensor`, `tf.Variable` or `str`) The x-tensor object or its name. x-tensor refers to the independent `tf.Tensor`, i.e., the tensor on the denominator of the differentiation. dump: A `tfdbg.DebugDumpDir` object. Returns: If this `GradientsDebugger` instance has the gradient tensor of `x_tensor` registered: a list of `numpy.ndarray` representing the value of the gradient tensor from `dump`. The list could be empty, if the gradient tensor is not executed in the `tf.Session.run()` call that generated the `dump`. The list could also contain multiple values of the gradient tensor, e.g., if gradient tensor is computed repeatedly in a `tf.while_loop` during the run that generated the `dump`. Raises: LookupError: If this `GradientsDebugger` instance does not have the gradient tensor of `x_tensor` registered. ValueError: If this `GradientsDebugger` has a `tf.Graph` object that does not match the `tf.Graph` object of the `dump`. TypeError: If `x_tensor` is not a `tf.Tensor`, `tf.Variable` or `str`. zmThis GradientsDebugger instance has a graph (%s) that differs from the graph of the DebugDumpDir object (%s).Z DebugIdentityN) Z python_graphr(r0rGrrrZ get_tensorsrZ'WatchKeyDoesNotExistInDebugDumpDirError)rYrKdumprGZ node_nameZ output_slotr r rgradient_values_from_dumpns    r])rTr9r Ztensorflow.python.debug.librrZtensorflow.python.frameworkrZtensorflow.python.opsrrr r$rrrrWZRegisterGradientrZr[r]r r r rs$