use resnet50 to train a cifar10 classifier

dvc.lock

@@ -0,0 +1,48 @@
+schema: '2.0'
+stages:
+  prepare:
+    cmd: python prepare.py
+    deps:
+    - path: prepare.py
+      hash: md5
+      md5: a1c07d1d5caf6e5288560a189415785c
+      size: 2979
+    params:
+      params.yaml:
+        prepare:
+          data_dir: data/raw
+          save_dir: data/processed
+          train_valid_split:
+          - 0.7
+          - 0.3
+          random_seed: 0
+    outs:
+    - path: data/processed
+      hash: md5
+      md5: f4bf62ffa725ca9144b7852a283dc1da.dir
+      size: 295118798
+      nfiles: 60000
+  train:
+    cmd: python train.py
+    deps:
+    - path: data/processed
+      hash: md5
+      md5: f4bf62ffa725ca9144b7852a283dc1da.dir
+      size: 295118798
+      nfiles: 60000
+    - path: train.py
+      hash: md5
+      md5: b797ccf2fe61952bbf6d83fa51b0b11f
+      size: 3407
+    params:
+      params.yaml:
+        train:
+          data_dir: data/processed
+          epochs: 5
+          batch_size: 256
+          learning_rate: 5e-05
+    outs:
+    - path: model.pt
+      hash: md5
+      md5: 8ead2a7cd52d70b359d3cdc3df5e43e3
+      size: 102592994