#print(pred_probs.shape)
test_df = pd.DataFrame(data=pred_probs.T, columns=breeds)
test_df.index = ids
if agg_test_df is None:
agg_test_df = test_df
else:
agg_test_df = agg_test_df.append(test_df)
except tf.errors.OutOfRangeError:
print('End of the dataset')
agg_test_df.to_csv(paths.TEST_PREDICTIONS,
index_label='id',
float_format='%.17f')
print('predictions saved to %s' % paths.TEST_PREDICTIONS)
if __name__ == '__main__':
with tf.Graph().as_default():
x = tf.placeholder(dtype=tf.float32,
shape=(consts.INCEPTION_CLASSES_COUNT, None),
name="x")
_, output_probs, _, _ = denseNN.denseNNModel(x,
consts.HEAD_MODEL_LAYERS,
gamma=0.01)
infer_test(consts.CURRENT_MODEL_NAME, output_probs, x)
print(one_hot_decoder(features['inception_output']))
print(features['label'])
print(features['inception_output'].shape)
if __name__ == '__main__':
with tf.Graph().as_default() as g, tf.Session().as_default() as sess:
tensors = unfreeze_into_current_graph(
paths.IMAGENET_GRAPH_DEF,
tensor_names=[
consts.INCEPTION_INPUT_TENSOR, consts.INCEPTION_OUTPUT_TENSOR
])
_, output_probs, y, _ = denseNN.denseNNModel(tf.reshape(
tensors[consts.INCEPTION_OUTPUT_TENSOR],
shape=(-1, 1),
name=consts.HEAD_INPUT_NODE_NAME),
consts.HEAD_MODEL_LAYERS,
gamma=0.01)
tf.global_variables_initializer().run()
saver = tf.train.Saver()
saver.restore(
sess, os.path.join(paths.CHECKPOINTS_DIR,
consts.CURRENT_MODEL_NAME))
freeze_current_model(consts.CURRENT_MODEL_NAME,
output_node_names=consts.OUTPUT_NODE_NAME)
if __name__ == '__main__':
convert(consts.CURRENT_MODEL_NAME, export_dir='/tmp/dogs_1')
batch_size=BATCH_SIZE,
train_sample_size=consts.TRAIN_SAMPLE_SIZE)
dev_set = sess.run(get_dev_ds)
dev_set_inception_output = dev_set[consts.INCEPTION_OUTPUT_FIELD]
dev_set_y_one_hot = dev_set[consts.LABEL_ONE_HOT_FIELD]
train_sample = sess.run(get_train_sample_ds)
train_sample_inception_output = train_sample[
consts.INCEPTION_OUTPUT_FIELD]
train_sample_y_one_hot = train_sample[consts.LABEL_ONE_HOT_FIELD]
x = tf.placeholder(dtype=tf.float32,
shape=(consts.INCEPTION_CLASSES_COUNT, None),
name="x")
cost, output_probs, y, nn_summaries = denseNN.denseNNModel(
x, consts.HEAD_MODEL_LAYERS, gamma=0.001)
optimizer = tf.train.AdamOptimizer(
learning_rate=LEARNING_RATE).minimize(cost)
dev_error_eval = error(x, output_probs, name='test_error')
train_error_eval = error(x, output_probs, name='train_error')
nn_merged_summaries = tf.summary.merge(nn_summaries)
tf.global_variables_initializer().run()
writer = tf.summary.FileWriter(
os.path.join(paths.SUMMARY_DIR, model_name))
bar = pyprind.ProgBar(EPOCHS_COUNT, update_interval=1, width=60)
saver = tf.train.Saver()