diff --git "a/AnimeDCGAN.ipynb" "b/AnimeDCGAN.ipynb"
--- "a/AnimeDCGAN.ipynb"
+++ "b/AnimeDCGAN.ipynb"
@@ -2,7 +2,7 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 1,
       "metadata": {
         "id": "n-HmiNnY3rlq"
       },
@@ -28,7 +28,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 2,
       "metadata": {
         "id": "_i8vg7XP52TJ"
       },
@@ -45,7 +45,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -71,7 +71,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -90,7 +90,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -125,7 +125,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -138,40 +138,40 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Model: \"sequential_4\"\n",
+            "Model: \"sequential\"\n",
             "_________________________________________________________________\n",
             " Layer (type)                Output Shape              Param #   \n",
             "=================================================================\n",
-            " conv2d_10 (Conv2D)          (None, 32, 32, 64)        1728      \n",
+            " conv2d (Conv2D)             (None, 32, 32, 64)        1728      \n",
             "                                                                 \n",
-            " leaky_re_lu_8 (LeakyReLU)   (None, 32, 32, 64)        0         \n",
+            " leaky_re_lu (LeakyReLU)     (None, 32, 32, 64)        0         \n",
             "                                                                 \n",
-            " conv2d_11 (Conv2D)          (None, 16, 16, 128)       73728     \n",
+            " conv2d_1 (Conv2D)           (None, 16, 16, 128)       73728     \n",
             "                                                                 \n",
-            " batch_normalization_14 (Bat  (None, 16, 16, 128)      512       \n",
-            " chNormalization)                                                \n",
+            " batch_normalization (BatchN  (None, 16, 16, 128)      512       \n",
+            " ormalization)                                                   \n",
             "                                                                 \n",
-            " leaky_re_lu_9 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
+            " leaky_re_lu_1 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
             "                                                                 \n",
-            " conv2d_12 (Conv2D)          (None, 8, 8, 256)         819200    \n",
+            " conv2d_2 (Conv2D)           (None, 8, 8, 256)         819200    \n",
             "                                                                 \n",
-            " batch_normalization_15 (Bat  (None, 8, 8, 256)        1024      \n",
-            " chNormalization)                                                \n",
+            " batch_normalization_1 (Batc  (None, 8, 8, 256)        1024      \n",
+            " hNormalization)                                                 \n",
             "                                                                 \n",
-            " leaky_re_lu_10 (LeakyReLU)  (None, 8, 8, 256)         0         \n",
+            " leaky_re_lu_2 (LeakyReLU)   (None, 8, 8, 256)         0         \n",
             "                                                                 \n",
-            " conv2d_13 (Conv2D)          (None, 4, 4, 512)         3276800   \n",
+            " conv2d_3 (Conv2D)           (None, 4, 4, 512)         3276800   \n",
             "                                                                 \n",
-            " batch_normalization_16 (Bat  (None, 4, 4, 512)        2048      \n",
-            " chNormalization)                                                \n",
+            " batch_normalization_2 (Batc  (None, 4, 4, 512)        2048      \n",
+            " hNormalization)                                                 \n",
             "                                                                 \n",
-            " leaky_re_lu_11 (LeakyReLU)  (None, 4, 4, 512)         0         \n",
+            " leaky_re_lu_3 (LeakyReLU)   (None, 4, 4, 512)         0         \n",
             "                                                                 \n",
-            " conv2d_14 (Conv2D)          (None, 1, 1, 1)           8192      \n",
+            " conv2d_4 (Conv2D)           (None, 1, 1, 1)           8192      \n",
             "                                                                 \n",
-            " flatten_2 (Flatten)         (None, 1)                 0         \n",
+            " flatten (Flatten)           (None, 1)                 0         \n",
             "                                                                 \n",
-            " activation_4 (Activation)   (None, 1)                 0         \n",
+            " activation (Activation)     (None, 1)                 0         \n",
             "                                                                 \n",
             "=================================================================\n",
             "Total params: 4,183,232\n",
@@ -213,7 +213,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 29,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -226,47 +226,47 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Model: \"sequential_5\"\n",
+            "Model: \"sequential_1\"\n",
             "_________________________________________________________________\n",
             " Layer (type)                Output Shape              Param #   \n",
             "=================================================================\n",
-            " dense_2 (Dense)             (None, 16384)             1654784   \n",
+            " dense (Dense)               (None, 16384)             1654784   \n",
             "                                                                 \n",
-            " batch_normalization_17 (Bat  (None, 16384)            65536     \n",
-            " chNormalization)                                                \n",
+            " batch_normalization_3 (Batc  (None, 16384)            65536     \n",
+            " hNormalization)                                                 \n",
             "                                                                 \n",
-            " re_lu_8 (ReLU)              (None, 16384)             0         \n",
+            " re_lu (ReLU)                (None, 16384)             0         \n",
             "                                                                 \n",
-            " reshape_2 (Reshape)         (None, 4, 4, 1024)        0         \n",
+            " reshape (Reshape)           (None, 4, 4, 1024)        0         \n",
             "                                                                 \n",
-            " conv2d_transpose_8 (Conv2DT  (None, 8, 8, 512)        13107200  \n",
-            " ranspose)                                                       \n",
+            " conv2d_transpose (Conv2DTra  (None, 8, 8, 512)        13107200  \n",
+            " nspose)                                                         \n",
             "                                                                 \n",
-            " batch_normalization_18 (Bat  (None, 8, 8, 512)        2048      \n",
-            " chNormalization)                                                \n",
+            " batch_normalization_4 (Batc  (None, 8, 8, 512)        2048      \n",
+            " hNormalization)                                                 \n",
             "                                                                 \n",
-            " re_lu_9 (ReLU)              (None, 8, 8, 512)         0         \n",
+            " re_lu_1 (ReLU)              (None, 8, 8, 512)         0         \n",
             "                                                                 \n",
-            " conv2d_transpose_9 (Conv2DT  (None, 16, 16, 256)      3276800   \n",
+            " conv2d_transpose_1 (Conv2DT  (None, 16, 16, 256)      3276800   \n",
             " ranspose)                                                       \n",
             "                                                                 \n",
-            " batch_normalization_19 (Bat  (None, 16, 16, 256)      1024      \n",
-            " chNormalization)                                                \n",
+            " batch_normalization_5 (Batc  (None, 16, 16, 256)      1024      \n",
+            " hNormalization)                                                 \n",
             "                                                                 \n",
-            " re_lu_10 (ReLU)             (None, 16, 16, 256)       0         \n",
+            " re_lu_2 (ReLU)              (None, 16, 16, 256)       0         \n",
             "                                                                 \n",
-            " conv2d_transpose_10 (Conv2D  (None, 32, 32, 128)      294912    \n",
-            " Transpose)                                                      \n",
+            " conv2d_transpose_2 (Conv2DT  (None, 32, 32, 128)      294912    \n",
+            " ranspose)                                                       \n",
             "                                                                 \n",
-            " batch_normalization_20 (Bat  (None, 32, 32, 128)      512       \n",
-            " chNormalization)                                                \n",
+            " batch_normalization_6 (Batc  (None, 32, 32, 128)      512       \n",
+            " hNormalization)                                                 \n",
             "                                                                 \n",
-            " re_lu_11 (ReLU)             (None, 32, 32, 128)       0         \n",
+            " re_lu_3 (ReLU)              (None, 32, 32, 128)       0         \n",
             "                                                                 \n",
-            " conv2d_transpose_11 (Conv2D  (None, 64, 64, 3)        3456      \n",
-            " Transpose)                                                      \n",
+            " conv2d_transpose_3 (Conv2DT  (None, 64, 64, 3)        3456      \n",
+            " ranspose)                                                       \n",
             "                                                                 \n",
-            " activation_5 (Activation)   (None, 64, 64, 3)         0         \n",
+            " activation_1 (Activation)   (None, 64, 64, 3)         0         \n",
             "                                                                 \n",
             "=================================================================\n",
             "Total params: 18,406,272\n",
@@ -309,7 +309,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 30,
+      "execution_count": 8,
       "metadata": {
         "id": "O0opsFYj3rlt"
       },
@@ -379,30 +379,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Requirement already satisfied: pydot in c:\\users\\franz\\.conda\\envs\\tf-gpu\\lib\\site-packages (1.4.2)\n",
-            "Requirement already satisfied: pyparsing>=2.1.4 in c:\\users\\franz\\.conda\\envs\\tf-gpu\\lib\\site-packages (from pydot) (3.0.7)\n",
-            "Requirement already satisfied: pydotplus in c:\\users\\franz\\.conda\\envs\\tf-gpu\\lib\\site-packages (2.0.2)\n",
-            "Requirement already satisfied: pyparsing>=2.0.1 in c:\\users\\franz\\.conda\\envs\\tf-gpu\\lib\\site-packages (from pydotplus) (3.0.7)\n",
-            "Requirement already satisfied: graphviz in c:\\users\\franz\\.conda\\envs\\tf-gpu\\lib\\site-packages (0.19.1)\n"
-          ]
-        }
-      ],
-      "source": [
-        "! pip install pydot\n",
-        "! pip install pydotplus\n",
-        "! pip install graphviz"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
+      "execution_count": 9,
       "metadata": {
         "id": "AUbyezGK3rlu"
       },
@@ -455,7 +432,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 38,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -463,7 +440,16 @@
         "id": "3AhjV0T_3rlu",
         "outputId": "a5cc1c5b-337d-4471-e96a-a1ab97062a9c"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\franz\\.conda\\envs\\tf-gpu\\lib\\site-packages\\keras\\optimizer_v2\\adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
+            "  super(Adam, self).__init__(name, **kwargs)\n"
+          ]
+        }
+      ],
       "source": [
         "gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)\n",
         "gan.compile(\n",
@@ -490,16 +476,16 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
+      "execution_count": 11,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1ff353090f0>"
+              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1f28584b130>"
             ]
           },
-          "execution_count": 34,
+          "execution_count": 11,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -517,21 +503,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 39,
+      "execution_count": 23,
       "metadata": {
         "id": "0QPNo21R3rlv"
       },
       "outputs": [
         {
-          "name": "stdout",
+          "name": "stderr",
           "output_type": "stream",
           "text": [
-            "1/1 [==============================] - 0s 64ms/step\n"
+            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
           ]
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -559,7 +545,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 40,
+      "execution_count": 24,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -639,18 +625,18 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 41,
+      "execution_count": 25,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "a200b305cdf34114a943e120ed2ce3ec",
+              "model_id": "8c2c123919ef4de9b3ce38f3f4300383",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "VBox(children=(GridspecLayout(children=(FloatSlider(value=0.13039077818393707, layout=Layout(grid_area='widget…"
+              "VBox(children=(GridspecLayout(children=(FloatSlider(value=-0.20623274147510529, layout=Layout(grid_area='widge…"
             ]
           },
           "metadata": {},
@@ -731,7 +717,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.0"
+      "version": "3.9.7"
     },
     "orig_nbformat": 4
   },