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Removing deprecated tensorflow api #76

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Removing deprecated tensorflow api #76

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8 changes: 3 additions & 5 deletions datagenerator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,16 +63,14 @@ def __init__(self, txt_file, mode, batch_size, num_classes, shuffle=True,
self.labels = convert_to_tensor(self.labels, dtype=dtypes.int32)

# create dataset
data = Dataset.from_tensor_slices((self.img_paths, self.labels))
data = tf.data.Dataset.from_tensor_slices((self.img_paths, self.labels))

# distinguish between train/infer. when calling the parsing functions
if mode == 'training':
data = data.map(self._parse_function_train, num_threads=8,
output_buffer_size=100*batch_size)
data = data.map(self._parse_function_train, num_parallel_calls=8).prefetch(100 * batch_size)

elif mode == 'inference':
data = data.map(self._parse_function_inference, num_threads=8,
output_buffer_size=100*batch_size)
data = data.map(self._parse_function_inference, num_parallel_calls=8).prefetch(100 * batch_size)

else:
raise ValueError("Invalid mode '%s'." % (mode))
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23 changes: 10 additions & 13 deletions finetune.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,8 @@ class on any given dataset. Specify the configuration settings at the
"""

# Path to the textfiles for the trainings and validation set
train_file = '/path/to/train.txt'
val_file = '/path/to/val.txt'
train_file = 'train.txt'
val_file = 'val.txt'

# Learning params
learning_rate = 0.01
Expand All @@ -44,8 +44,8 @@ class on any given dataset. Specify the configuration settings at the
display_step = 20

# Path for tf.summary.FileWriter and to store model checkpoints
filewriter_path = "/tmp/finetune_alexnet/tensorboard"
checkpoint_path = "/tmp/finetune_alexnet/checkpoints"
filewriter_path = "tmp/tensorboard"
checkpoint_path = "tmp/checkpoints"

"""
Main Part of the finetuning Script.
Expand Down Expand Up @@ -93,8 +93,8 @@ class on any given dataset. Specify the configuration settings at the

# Op for calculating the loss
with tf.name_scope("cross_ent"):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=score,
labels=y))
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=score,
labels=y))

# Train op
with tf.name_scope("train"):
Expand All @@ -117,7 +117,6 @@ class on any given dataset. Specify the configuration settings at the
# Add the loss to summary
tf.summary.scalar('cross_entropy', loss)


# Evaluation op: Accuracy of the model
with tf.name_scope("accuracy"):
correct_pred = tf.equal(tf.argmax(score, 1), tf.argmax(y, 1))
Expand All @@ -136,12 +135,11 @@ class on any given dataset. Specify the configuration settings at the
saver = tf.train.Saver()

# Get the number of training/validation steps per epoch
train_batches_per_epoch = int(np.floor(tr_data.data_size/batch_size))
train_batches_per_epoch = int(np.floor(tr_data.data_size / batch_size))
val_batches_per_epoch = int(np.floor(val_data.data_size / batch_size))

# Start Tensorflow session
with tf.Session() as sess:

# Initialize all variables
sess.run(tf.global_variables_initializer())

Expand All @@ -158,7 +156,7 @@ class on any given dataset. Specify the configuration settings at the
# Loop over number of epochs
for epoch in range(num_epochs):

print("{} Epoch number: {}".format(datetime.now(), epoch+1))
print("{} Epoch number: {}".format(datetime.now(), epoch + 1))

# Initialize iterator with the training dataset
sess.run(training_init_op)
Expand All @@ -179,15 +177,14 @@ class on any given dataset. Specify the configuration settings at the
y: label_batch,
keep_prob: 1.})

writer.add_summary(s, epoch*train_batches_per_epoch + step)
writer.add_summary(s, epoch * train_batches_per_epoch + step)

# Validate the model on the entire validation set
print("{} Start validation".format(datetime.now()))
sess.run(validation_init_op)
test_acc = 0.
test_count = 0
for _ in range(val_batches_per_epoch):

img_batch, label_batch = sess.run(next_batch)
acc = sess.run(accuracy, feed_dict={x: img_batch,
y: label_batch,
Expand All @@ -201,7 +198,7 @@ class on any given dataset. Specify the configuration settings at the

# save checkpoint of the model
checkpoint_name = os.path.join(checkpoint_path,
'model_epoch'+str(epoch+1)+'.ckpt')
'model_epoch' + str(epoch + 1) + '.ckpt')
save_path = saver.save(sess, checkpoint_name)

print("{} Model checkpoint saved at {}".format(datetime.now(),
Expand Down