ques_emb = tf.tanh(ques_emb_drop)
output, state = stacked_lstm(ques_emb, state)
cnn_mean, cnn_var = tf.nn.moments(image, [0])
cnn_out_true_n = tf.nn.batch_normalization(
image, cnn_mean, cnn_var, None, None,
epsilon) #,var_dict['cnnoutbeta'],
#var_dict['cnnoutscale'],epsilon)
rnn_mean, rnn_var = tf.nn.moments(state, [0])
rnn_out_true_n = tf.nn.batch_normalization(
state, rnn_mean, rnn_var, None, None,
epsilon) #var_dict['rnnoutbeta'],
features = combine_embeddings(cnn_out_true_n, rnn_out_true_n, var_dict)
#features = tf.concat([image,state], 1)
#features = tf.concat([state,image], 1)
features2 = tf.concat([features, noise], 1)
scores_emb = generator_me(features2, var_dict)
#v1 = tf.Variable(tf.truncated_normal([2048,1000]))
#v2 = tf.Variable(tf.constant(0.1, shape=[1000]))
#scores_emb = tf.nn.relu_layer(state, v1, v2)
# saver = tf.train.Saver()
# saver.restore(sess, "./train_g_v3.3")
# #var_dict['gbeta'] = tf.Variable(tf.zeros([1000]), name='gbeta')
for i in range(max_words_q):
if i == 0:
ques_emb_linear = tf.zeros([batch_size, input_embedding_size])
else:
tf.get_variable_scope().reuse_variables()
ques_emb_linear = tf.nn.embedding_lookup(var_dict['rnnqW'],
question[:, i - 1])
#ques_emb_linear = tf.gather(var_dict['rnnqW'], question[:,i-1])
ques_emb_drop = tf.nn.dropout(ques_emb_linear, 1 - drop_out_rate)
ques_emb = tf.tanh(ques_emb_drop)
output, state = stacked_lstm(ques_emb, state)
print(state.get_shape())
features = combine_embeddings(image, state, var_dict)
#features = tf.concat([image,state], 1)
#features = tf.concat([state,image], 1)
features2 = tf.concat([features, noise], 1)
scores_emb = generator_me(features2, var_dict)
#v1 = tf.Variable(tf.truncated_normal([2048,1000]))
#v2 = tf.Variable(tf.constant(0.1, shape=[1000]))
#scores_emb = tf.nn.relu_layer(state, v1, v2)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=scores_emb,
labels=answers_true))
tvars = tf.trainable_variables()
output, state = stacked_lstm(ques_emb, state)
cnn_mean, cnn_var = tf.nn.moments(image, [0])
cnn_out_true_n = tf.nn.batch_normalization(
image, cnn_mean, cnn_var, None, None,
epsilon) #,var_dict['cnnoutbeta'],
#var_dict['cnnoutscale'],epsilon)
rnn_mean, rnn_var = tf.nn.moments(state, [0])
rnn_out_true_n = tf.nn.batch_normalization(
state, rnn_mean, rnn_var, None, None,
epsilon) #var_dict['rnnoutbeta'],
#var_dict['rnnoutscale'],epsilon)
features = combine_embeddings(cnn_out_true_n, rnn_out_true_n, var_dict)
#features = tf.concat([image,state], 1)
#features = tf.concat([state,image], 1)
#features2 = tf.concat([features,noise], 1)
features2 = tf.add(features, noise)
scores_emb = generator_me(features2, var_dict)
#v1 = tf.Variable(tf.truncated_normal([2048,1000]))
#v2 = tf.Variable(tf.constant(0.1, shape=[1000]))
#scores_emb = tf.nn.relu_layer(state, v1, v2)
#saver = tf.train.Saver()
#saver.restore(sess, "./train_g_v3.2.2")
