add diagrams and temporary masa reader for presentation (637252fc) · Commits · Softwareprojekt 2018 - Jason Marinco Rudolf Samuel / O Caption My Caption

.gitignore

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		#cocoapi
		cocoapi/
		cnn_exercise/
		anaconda3/

		#lstm worked files
		capiton-lib/lstm/Input_Data

Diagrams/Pipeline.png

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Diagrams/Pipeline.xml

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		<mxfile userAgent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.181 Safari/537.36" version="8.6.8" editor="www.draw.io" type="device"><diagram name="Page-1" id="e56a1550-8fbb-45ad-956c-1786394a9013">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</diagram></mxfile>
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		<mxfile userAgent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.79 Safari/537.36" version="8.7.7" editor="www.draw.io" type="device"><diagram name="Page-1" id="e56a1550-8fbb-45ad-956c-1786394a9013">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</diagram></mxfile>
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Diagrams/spezifikationsvortrag-fur-das.pdf

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caption-lib/lstm/Trainers/masa_trainer.py

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		@@ -15,7 +15,7 @@ class masa_trainer:
		# create the main model
		class Model(object):
		def __init__(self, input, is_training, hidden_size, vocab_size, num_layers,
		dropout=0.5, init_scale=0.05):
		dropout=config.trainer.dropout, init_scale=config.trainer.init_scale):
		self.is_training = is_training
		self.input_obj = input
		self.batch_size = input.batch_size
		@@ -37,7 +37,7 @@ class masa_trainer:
		rnn_tuple_state = tuple([tf.contrib.rnn.LSTMStateTuple(state_per_layer_list[idx][0], state_per_layer_list[idx][1])for idx in range(num_layers)])

		# create an LSTM cell to be unrolled
		cell = tf.contrib.rnn.LSTMCell(hidden_size, forget_bias=1.0)
		cell = tf.contrib.rnn.LSTMCell(hidden_size, forget_bias=config.trainer.forget_bias)
		# add a dropout wrapper if training
		if is_training and dropout < 1:
		cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=dropout)
		@@ -89,10 +89,10 @@ class masa_trainer:
		session.run(self.lr_update, feed_dict={self.new_lr: lr_value})

		def train(self, train_data, vocabulary, num_layers, num_epochs, batch_size,
		learning_rate=1.0, max_lr_epoch=10, lr_decay=0.93):
		learning_rate=config.trainer.learning_rate, max_lr_epoch=10, lr_decay=0.93):
		# setup data and models
		training_input = self.Input(batch_size=batch_size, num_steps=35, data=train_data)
		m = self.Model(training_input, is_training=True, hidden_size=650, vocab_size=vocabulary,
		training_input = self.Input(batch_size=batch_size, num_steps=config.trainer.num_steps, data=train_data)
		m = self.Model(training_input, is_training=True, hidden_size=config.trainer.hidden_size, vocab_size=vocabulary,
		num_layers=num_layers)
		init_op = tf.global_variables_initializer()

		@@ -124,9 +124,9 @@ class masa_trainer:
		coord.join(threads)

		def test(self, model_path, test_data, reversed_dictionary):
		test_input = self.Input(batch_size=20, num_steps=35, data=test_data)
		m = self.Model(test_input, is_training=False, hidden_size=650, vocab_size=config.trainer.vocab_size,
		num_layers=2)
		test_input = self.Input(batch_size=config.trainer.batch_size, num_steps=config.trainer.num_steps, data=test_data)
		m = self.Model(test_input, is_training=False, hidden_size=config.trainer.hidden_size, vocab_size=config.trainer.vocab_size,
		num_layers=config.trainer.num_layers)
		saver = tf.train.Saver()
		with tf.Session() as sess:
		# start threads