Error in model performance metrics - python-3.x

Question

Well my neural network is as follows :
# Leaks data input is a 2-D vector of window*size*341 features
# Reshape to match picture format [Height x Width x Channel]
# Tensor input become 4-D: [Batch Size, Height, Width, Channel]
x = tf.reshape(x, shape= [-1, 16, 341, 2])
# Convolution Layer with 32 filters and a kernel size of 5
conv1 = tf.layers.conv2d(x, 6, 2, activation=tf.nn.relu)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
# Convolution Layer with 64 filters and a kernel size of 3
conv2 = tf.layers.conv2d(conv1, 8, 3, activation=tf.nn.relu)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
conv2 = tf.layers.max_pooling2d(conv2, 2, 2)
# Flatten the data to a 1-D vector for the fully connected layer
fc1 = tf.contrib.layers.flatten(conv2)
# Fully connected layer (in tf contrib folder for now)
fc1 = tf.layers.dense(fc1, 1024)
# Apply Dropout (if is_training is False, dropout is not applied)
fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
# 1-layer LSTM with n_hidden units.
out = tf.layers.dense(fc1, n_classes)
it predicts a multi-label classification vector on len = 339, first i wanted to make sure that i'm fully able to overfit small sample of data to make sure that every thing work okey and well defined.
I trained my neural network on 1700 len data,to measure my model performance i added accuracy as follow :
logits_train = conv_net(features, num_classes, dropout, reuse=False,
is_training=True)
logits_test = conv_net(features, num_classes, dropout, reuse=True,
is_training=False)
# Predictions
pred_classes = tf.cast(tf.greater(logits_test,0.5), tf.float32)
pred_probas = tf.nn.sigmoid(logits_test)
# If prediction mode, early return
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode, predictions=pred_classes)
# Define loss and optimizer
#tf.one_hot(tf.cast(labels,dtype=tf.int32),depth=2)
loss_op = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.cast(labels,dtype=tf.float32),logits=logits_train))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op,global_step=tf.train.get_global_step())
# Evaluate the accuracy of the model
accuracy = tf.metrics.accuracy(labels=labels , predictions = pred_classes )
#correct_prediction = tf.equal(tf.round(tf.nn.sigmoid(logits_test)), tf.round(labels))
#accuracy1 = tf.metrics.mean(tf.cast(correct_prediction, tf.float32))
#acc_op = tf.metrics.mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pred_classes,labels=labels))
# TF Estimators requires to return a EstimatorSpec, that specify
# the different ops for training, evaluating, ...
estim_specs = tf.estimator.EstimatorSpec(
mode=mode,
predictions=pred_probas,
loss=loss_op,
train_op=train_op,
eval_metric_ops={'accuracy': accuracy})
return estim_specs
The problem is that with few epochs the performance seems to be very good
for i in range(1,50):
print('Epoch',(i+1))
input_fn = tf.estimator.inputs.numpy_input_fn(x= curr_data_batch,y=curr_target_batch[:,:339] ,batch_size=96, shuffle=False)
model.train(input_fn=input_fn)
if (i+1) % 10 :
# eval the model
eval_model = model.evaluate(input_fn=input_fn)
print('Loss ,',eval_model['loss'] )
print('accuracy ,',eval_model['accuracy'] )
Loss , 0.029562088
accuracy , 0.9958855
Epoch 3:
Loss , 0.028194984
accuracy , 0.99588597
Epoch 4:
Loss , 0.027557796
accuracy , 0.9958862
but when i try to predict same training data i got fully oposet metrics
loss = 0.65
accuracy = 0.33
I don't know where this issue come from did i miss defined something or no ?
Ty