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Self.loss = tf.reduce_mean(self.Library IEEE use IEEE.
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# print("self.per_example_loss.shape:",self.per_example_loss.shape) Self.per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1 )
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One_hot_labels = tf.one_hot(self.input_relation, depth=num_labels, dtype=tf.float32) # print(one_hot_labels) # print("one_hot_labels.shape:",one_hot_labels.shape) Self.predictions = tf.argmax(self.logits, axis=-1, name= " predictions " ) Log_probs = tf.nn.log_softmax(self.logits, axis=-1) # print( " log_probs.shape: " ,log_probs.shape) Self.probabilities = tf.nn.softmax(self.logits, axis=-1 ) Self.logits = tf.nn.bias_add(logits, output_bias) Logits = tf.matmul(pooled_layer, output_weights, transpose_b=True) # * = # print("logits.shape:",logits.shape)
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" output_bias ",, initializer=tf.zeros_initializer()) # Initializer=tf.truncated_normal_initializer(stddev=0.02)) # print( " output_weights.shape: " ,output_weights) Print( " pooled_output.shape: " ,pooled_output.shape) Pooled_output = tf.squeeze(pooled_output, axis=2 ) Pooled_output = tf.matmul(tf.expand_dims(layer_dist, axis=2 ), seq_out) Print( " seq_out.shape: " ,seq_out.shape) Layer_dist = tf.nn.softmax(layer_logits) # print( " layer_dist.shape: " ,layer_dist.shape) Layer_logits = tf.concat(layer_logits, axis=2) # 第三维度拼接 print( " layer_logits.shape: " ,layer_logits.shape) Print( " np.array(layer_logits).shape: " ,np.array(layer_logits).shape) Kernel_initializer=tf.truncated_normal_initializer(stddev=0.02 ), Hidden_size = output_ # 768 print( " = " )įor i, layer in enumerate(model.all_encoder_layers): Print( " output_layer.shape: " ,output_layer.shape) Output_layer = model.get_sequence_output() # If you want to use the token-level output, use model.get_sequence_output() # output_layer = model.get_pooled_output() # print("output_layer.shape:",output_layer) Bert_config_file = _config_fileīert_config = om_json_file(bert_config_file)