diff --git a/my_runs/1/config.json b/my_runs/1/config.json
new file mode 100644
index 0000000..f796e16
--- /dev/null
+++ b/my_runs/1/config.json
@@ -0,0 +1,5 @@
+{
+  "dropout_layer_value": 0.4,
+  "num_epochs": 100,
+  "seed": 512638064
+}
\ No newline at end of file
diff --git a/my_runs/1/cout.txt b/my_runs/1/cout.txt
new file mode 100644
index 0000000..6da2078
--- /dev/null
+++ b/my_runs/1/cout.txt
@@ -0,0 +1,213 @@
+2.16.1
+1.2.0
+3.2.1
+1.23.5
+1.5.2
+C:\Users\obses\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\preprocessing\_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.
+  warnings.warn(
+C:\Users\obses\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
+  super().__init__(activity_regularizer=activity_regularizer, **kwargs)
+Epoch 1/100
+2/2 - 1s - 340ms/step - accuracy: 0.4390 - loss: 2.1215 - val_accuracy: 0.6429 - val_loss: 2.0350
+Epoch 2/100
+2/2 - 0s - 23ms/step - accuracy: 0.3659 - loss: 2.0694 - val_accuracy: 0.7143 - val_loss: 2.0104
+Epoch 3/100
+2/2 - 0s - 22ms/step - accuracy: 0.3659 - loss: 2.1309 - val_accuracy: 0.8214 - val_loss: 1.9882
+Epoch 4/100
+2/2 - 0s - 23ms/step - accuracy: 0.4390 - loss: 2.0289 - val_accuracy: 0.8214 - val_loss: 1.9593
+Epoch 5/100
+2/2 - 0s - 21ms/step - accuracy: 0.6341 - loss: 1.9654 - val_accuracy: 0.8214 - val_loss: 1.9378
+Epoch 6/100
+2/2 - 0s - 22ms/step - accuracy: 0.6098 - loss: 2.0383 - val_accuracy: 0.8214 - val_loss: 1.9154
+Epoch 7/100
+2/2 - 0s - 22ms/step - accuracy: 0.6098 - loss: 2.0238 - val_accuracy: 0.8214 - val_loss: 1.8964
+Epoch 8/100
+2/2 - 0s - 23ms/step - accuracy: 0.6098 - loss: 1.9397 - val_accuracy: 0.8571 - val_loss: 1.8766
+Epoch 9/100
+2/2 - 0s - 25ms/step - accuracy: 0.5366 - loss: 1.9641 - val_accuracy: 0.8571 - val_loss: 1.8561
+Epoch 10/100
+2/2 - 0s - 23ms/step - accuracy: 0.5610 - loss: 1.9581 - val_accuracy: 0.8571 - val_loss: 1.8380
+Epoch 11/100
+2/2 - 0s - 23ms/step - accuracy: 0.7561 - loss: 1.9044 - val_accuracy: 0.8571 - val_loss: 1.8207
+Epoch 12/100
+2/2 - 0s - 23ms/step - accuracy: 0.5854 - loss: 1.9392 - val_accuracy: 0.8571 - val_loss: 1.8004
+Epoch 13/100
+2/2 - 0s - 24ms/step - accuracy: 0.7073 - loss: 1.8718 - val_accuracy: 0.8571 - val_loss: 1.7812
+Epoch 14/100
+2/2 - 0s - 22ms/step - accuracy: 0.7561 - loss: 1.8057 - val_accuracy: 0.8571 - val_loss: 1.7620
+Epoch 15/100
+2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.8354 - val_accuracy: 0.8571 - val_loss: 1.7440
+Epoch 16/100
+2/2 - 0s - 24ms/step - accuracy: 0.7073 - loss: 1.8501 - val_accuracy: 0.8571 - val_loss: 1.7269
+Epoch 17/100
+2/2 - 0s - 24ms/step - accuracy: 0.7561 - loss: 1.7831 - val_accuracy: 0.8571 - val_loss: 1.7084
+Epoch 18/100
+2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.7120 - val_accuracy: 0.8571 - val_loss: 1.6931
+Epoch 19/100
+2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.8020 - val_accuracy: 0.8571 - val_loss: 1.6786
+Epoch 20/100
+2/2 - 0s - 24ms/step - accuracy: 0.8049 - loss: 1.7531 - val_accuracy: 0.8571 - val_loss: 1.6630
+Epoch 21/100
+2/2 - 0s - 21ms/step - accuracy: 0.7561 - loss: 1.7808 - val_accuracy: 0.8571 - val_loss: 1.6489
+Epoch 22/100
+2/2 - 0s - 21ms/step - accuracy: 0.7561 - loss: 1.7794 - val_accuracy: 0.8571 - val_loss: 1.6352
+Epoch 23/100
+2/2 - 0s - 23ms/step - accuracy: 0.7805 - loss: 1.6697 - val_accuracy: 0.8571 - val_loss: 1.6184
+Epoch 24/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.6814 - val_accuracy: 0.8571 - val_loss: 1.6058
+Epoch 25/100
+2/2 - 0s - 29ms/step - accuracy: 0.8293 - loss: 1.6687 - val_accuracy: 0.8571 - val_loss: 1.5919
+Epoch 26/100
+2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.7052 - val_accuracy: 0.8571 - val_loss: 1.5786
+Epoch 27/100
+2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.6147 - val_accuracy: 0.8571 - val_loss: 1.5663
+Epoch 28/100
+2/2 - 0s - 21ms/step - accuracy: 0.7805 - loss: 1.6207 - val_accuracy: 0.8571 - val_loss: 1.5529
+Epoch 29/100
+2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5964 - val_accuracy: 0.8571 - val_loss: 1.5413
+Epoch 30/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6258 - val_accuracy: 0.8571 - val_loss: 1.5294
+Epoch 31/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5512 - val_accuracy: 0.8571 - val_loss: 1.5175
+Epoch 32/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6674 - val_accuracy: 0.8571 - val_loss: 1.5070
+Epoch 33/100
+2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5848 - val_accuracy: 0.8571 - val_loss: 1.4977
+Epoch 34/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5467 - val_accuracy: 0.8571 - val_loss: 1.4869
+Epoch 35/100
+2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.6244 - val_accuracy: 0.8571 - val_loss: 1.4778
+Epoch 36/100
+2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.5275 - val_accuracy: 0.8571 - val_loss: 1.4672
+Epoch 37/100
+2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 1.7105 - val_accuracy: 0.8571 - val_loss: 1.4595
+Epoch 38/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.6137 - val_accuracy: 0.8571 - val_loss: 1.4509
+Epoch 39/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5726 - val_accuracy: 0.8571 - val_loss: 1.4425
+Epoch 40/100
+2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.5345 - val_accuracy: 0.8571 - val_loss: 1.4347
+Epoch 41/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5689 - val_accuracy: 0.8571 - val_loss: 1.4269
+Epoch 42/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4239 - val_accuracy: 0.8571 - val_loss: 1.4175
+Epoch 43/100
+2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.5922 - val_accuracy: 0.8571 - val_loss: 1.4099
+Epoch 44/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5006 - val_accuracy: 0.8571 - val_loss: 1.4021
+Epoch 45/100
+2/2 - 0s - 21ms/step - accuracy: 0.8049 - loss: 1.4858 - val_accuracy: 0.8571 - val_loss: 1.3944
+Epoch 46/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4769 - val_accuracy: 0.8571 - val_loss: 1.3874
+Epoch 47/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4211 - val_accuracy: 0.8571 - val_loss: 1.3796
+Epoch 48/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4060 - val_accuracy: 0.8571 - val_loss: 1.3717
+Epoch 49/100
+2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4741 - val_accuracy: 0.8571 - val_loss: 1.3652
+Epoch 50/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4989 - val_accuracy: 0.8571 - val_loss: 1.3588
+Epoch 51/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4718 - val_accuracy: 0.8571 - val_loss: 1.3521
+Epoch 52/100
+2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.4712 - val_accuracy: 0.8571 - val_loss: 1.3482
+Epoch 53/100
+2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.3657 - val_accuracy: 0.8571 - val_loss: 1.3425
+Epoch 54/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3847 - val_accuracy: 0.8571 - val_loss: 1.3366
+Epoch 55/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3766 - val_accuracy: 0.8571 - val_loss: 1.3315
+Epoch 56/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4275 - val_accuracy: 0.8571 - val_loss: 1.3253
+Epoch 57/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4709 - val_accuracy: 0.8571 - val_loss: 1.3209
+Epoch 58/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4226 - val_accuracy: 0.8571 - val_loss: 1.3176
+Epoch 59/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4491 - val_accuracy: 0.8571 - val_loss: 1.3126
+Epoch 60/100
+2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.4165 - val_accuracy: 0.8571 - val_loss: 1.3073
+Epoch 61/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3914 - val_accuracy: 0.8571 - val_loss: 1.3027
+Epoch 62/100
+2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3560 - val_accuracy: 0.8571 - val_loss: 1.2974
+Epoch 63/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3432 - val_accuracy: 0.8571 - val_loss: 1.2915
+Epoch 64/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3833 - val_accuracy: 0.8571 - val_loss: 1.2866
+Epoch 65/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4477 - val_accuracy: 0.8571 - val_loss: 1.2834
+Epoch 66/100
+2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3054 - val_accuracy: 0.8571 - val_loss: 1.2794
+Epoch 67/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2840 - val_accuracy: 0.8571 - val_loss: 1.2742
+Epoch 68/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4320 - val_accuracy: 0.8571 - val_loss: 1.2703
+Epoch 69/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4165 - val_accuracy: 0.8571 - val_loss: 1.2664
+Epoch 70/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3210 - val_accuracy: 0.8571 - val_loss: 1.2617
+Epoch 71/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3698 - val_accuracy: 0.8571 - val_loss: 1.2580
+Epoch 72/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3679 - val_accuracy: 0.8571 - val_loss: 1.2546
+Epoch 73/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3647 - val_accuracy: 0.8571 - val_loss: 1.2506
+Epoch 74/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3088 - val_accuracy: 0.8571 - val_loss: 1.2465
+Epoch 75/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3304 - val_accuracy: 0.8571 - val_loss: 1.2425
+Epoch 76/100
+2/2 - 0s - 29ms/step - accuracy: 0.8537 - loss: 1.2813 - val_accuracy: 0.8571 - val_loss: 1.2389
+Epoch 77/100
+2/2 - 0s - 28ms/step - accuracy: 0.8537 - loss: 1.3477 - val_accuracy: 0.8571 - val_loss: 1.2352
+Epoch 78/100
+2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.3885 - val_accuracy: 0.8571 - val_loss: 1.2315
+Epoch 79/100
+2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2939 - val_accuracy: 0.8571 - val_loss: 1.2280
+Epoch 80/100
+2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.3508 - val_accuracy: 0.8571 - val_loss: 1.2254
+Epoch 81/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3219 - val_accuracy: 0.8571 - val_loss: 1.2223
+Epoch 82/100
+2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.3559 - val_accuracy: 0.8571 - val_loss: 1.2190
+Epoch 83/100
+2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3289 - val_accuracy: 0.8571 - val_loss: 1.2159
+Epoch 84/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1656 - val_accuracy: 0.8571 - val_loss: 1.2123
+Epoch 85/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2954 - val_accuracy: 0.8571 - val_loss: 1.2088
+Epoch 86/100
+2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2298 - val_accuracy: 0.8571 - val_loss: 1.2058
+Epoch 87/100
+2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3323 - val_accuracy: 0.8571 - val_loss: 1.2037
+Epoch 88/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3758 - val_accuracy: 0.8571 - val_loss: 1.2010
+Epoch 89/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3587 - val_accuracy: 0.8571 - val_loss: 1.1981
+Epoch 90/100
+2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3240 - val_accuracy: 0.8571 - val_loss: 1.1954
+Epoch 91/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2884 - val_accuracy: 0.8571 - val_loss: 1.1922
+Epoch 92/100
+2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3293 - val_accuracy: 0.8571 - val_loss: 1.1901
+Epoch 93/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2706 - val_accuracy: 0.8571 - val_loss: 1.1879
+Epoch 94/100
+2/2 - 0s - 27ms/step - accuracy: 0.8537 - loss: 1.2715 - val_accuracy: 0.8571 - val_loss: 1.1848
+Epoch 95/100
+2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2628 - val_accuracy: 0.8571 - val_loss: 1.1818
+Epoch 96/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2770 - val_accuracy: 0.8571 - val_loss: 1.1786
+Epoch 97/100
+2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3039 - val_accuracy: 0.8571 - val_loss: 1.1762
+Epoch 98/100
+2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 1.2908 - val_accuracy: 0.8571 - val_loss: 1.1731
+Epoch 99/100
+2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.3510 - val_accuracy: 0.8571 - val_loss: 1.1707
+Epoch 100/100
+2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2447 - val_accuracy: 0.8571 - val_loss: 1.1683
+1/1 - 0s - 26ms/step - accuracy: 0.8571 - loss: 1.1683
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+Dokładność testowa: 85.71%
+
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8571 - loss: 1.1683
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.8571 - loss: 1.1683
diff --git a/my_runs/1/info.json b/my_runs/1/info.json
new file mode 100644
index 0000000..7b6f1a0
--- /dev/null
+++ b/my_runs/1/info.json
@@ -0,0 +1,15 @@
+{
+  "dropout_layer_value": 0.4,
+  "num_epochs": 100,
+  "training_ts": {
+    "__reduce__": [
+      {
+        "py/type": "datetime.datetime"
+      },
+      [
+        "B+gGCQ06DAHl8g=="
+      ]
+    ],
+    "py/object": "datetime.datetime"
+  }
+}
\ No newline at end of file
diff --git a/my_runs/1/metrics.json b/my_runs/1/metrics.json
new file mode 100644
index 0000000..e1b52a5
--- /dev/null
+++ b/my_runs/1/metrics.json
@@ -0,0 +1,248 @@
+{
+  "test accuracy": {
+    "steps": [
+      0
+    ],
+    "timestamps": [
+      "2024-06-09T11:58:17.715614"
+    ],
+    "values": [
+      0.8571428656578064
+    ]
+  },
+  "test loss": {
+    "steps": [
+      0
+    ],
+    "timestamps": [
+      "2024-06-09T11:58:17.715614"
+    ],
+    "values": [
+      1.168319582939148
+    ]
+  },
+  "train accuracy": {
+    "steps": [
+      0
+    ],
+    "timestamps": [
+      "2024-06-09T11:58:17.715614"
+    ],
+    "values": [
+      [
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+        0.3658536672592163,
+        0.3658536672592163,
+        0.4390243887901306,
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+        0.6097561120986938,
+        0.5365853905677795,
+        0.5609756112098694,
+        0.7560975551605225,
+        0.5853658318519592,
+        0.707317054271698,
+        0.7560975551605225,
+        0.8048780560493469,
+        0.707317054271698,
+        0.7560975551605225,
+        0.8536585569381714,
+        0.8048780560493469,
+        0.8048780560493469,
+        0.7560975551605225,
+        0.7560975551605225,
+        0.7804877758026123,
+        0.8536585569381714,
+        0.8292682766914368,
+        0.8292682766914368,
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+        0.7804877758026123,
+        0.8292682766914368,
+        0.8536585569381714,
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\ No newline at end of file
diff --git a/my_runs/1/model.keras b/my_runs/1/model.keras
new file mode 100644
index 0000000..50ff287
Binary files /dev/null and b/my_runs/1/model.keras differ