diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index b1e97add..e0c863cc 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -9,7 +9,6 @@ the below items:
 - [ ] Code is [PEP8](https://www.python.org/dev/peps/pep-0008/)
 - [ ] I'm making the PR to `master`
 - [ ] I've updated the versions based on [Semantic Versioning](https://semver.org/)
-  - [ ] `setup.py`
   - [ ] `talos/__init__.py`
   - [ ] `docs/index.html`
   - [ ] `docs/_coverpage.md`
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 88d97d38..6a88f46a 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 9
       matrix:
-        python-version: [3.7, 3.8]
+        python-version: ['3.9', '3.10', '3.11']
         os: [ubuntu-latest, macos-latest]
 
     steps:
@@ -33,7 +33,7 @@ jobs:
     - name: Tests
       run: |
         export MPLBACKEND=agg
-        pip install tensorflow>=2.0
+        pip install tensorflow==2.14.1
         pip install coveralls
         coverage run --source=talos ./test-ci.py
     - name: Coverage
diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 21f2f01d..afef4e0c 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -16,11 +16,11 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install setuptools wheel twine
+        pip install hatch
     - name: Build and publish
       env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+        PYPI_API_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
       run: |
-        python setup.py sdist bdist_wheel
-        twine upload dist/*
+        hatch build
+        hatch publish --user __token__ --auth $PYPI_API_TOKEN
+
diff --git a/.gitignore b/.gitignore
index a58fe15d..fc4ba81f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,4 +14,5 @@ talos.egg-info
 *.npy
 test.sh
 .vscode
-.test_*
\ No newline at end of file
+test_*/
+test/
\ No newline at end of file
diff --git a/docs/Examples_Generator.md b/docs/Examples_Generator.md
index 32826327..003d4514 100644
--- a/docs/Examples_Generator.md
+++ b/docs/Examples_Generator.md
@@ -83,8 +83,8 @@ return out, model
 ### Parameter Dictionary
 ```python
 p = {'activation':['relu', 'elu'],
-     'optimizer': ['AdaDelta'],
-     'losses': ['logcosh'],
+     'optimizer': ['Adam'],
+     'losses': ['LogCosh'],
      'shapes': ['brick'],
      'first_neuron': [32],
      'dropout': [.2, .3],
diff --git a/docs/Examples_Generator_Code.md b/docs/Examples_Generator_Code.md
index 010a53cd..d0c8cde5 100644
--- a/docs/Examples_Generator_Code.md
+++ b/docs/Examples_Generator_Code.md
@@ -36,8 +36,8 @@ def mnist_model(x_train, y_train, x_val, y_val, params):
     return out, model
 
 p = {'activation':['relu', 'elu'],
-     'optimizer': ['AdaDelta'],
-     'losses': ['logcosh'],
+     'optimizer': ['Adam'],
+     'losses': ['LogCosh'],
      'shapes': ['brick'],
      'first_neuron': [32],
      'dropout': [.2, .3],
diff --git a/docs/Examples_PyTorch.md b/docs/Examples_PyTorch.md
index dc8d468e..1ad48723 100644
--- a/docs/Examples_PyTorch.md
+++ b/docs/Examples_PyTorch.md
@@ -138,8 +138,8 @@ return net, net.parameters()
 
 ```python
 p = {'activation':['relu', 'elu'],
-       'optimizer': ['Nadam', 'Adam'],
-       'losses': ['logcosh'],
+       'optimizer': ['Adagrad', 'Adam'],
+       'losses': ['LogCosh'],
        'hidden_layers':[0, 1, 2],
        'batch_size': (20, 50, 5),
        'epochs': [10, 20]}
diff --git a/docs/Examples_PyTorch_Code.md b/docs/Examples_PyTorch_Code.md
index bc697299..480185b8 100644
--- a/docs/Examples_PyTorch_Code.md
+++ b/docs/Examples_PyTorch_Code.md
@@ -106,8 +106,8 @@ def breast_cancer(x_train, y_train, x_val, y_val, params):
 
 
 p = {'activation':['relu', 'elu'],
-       'optimizer': ['Nadam', 'Adam'],
-       'losses': ['logcosh'],
+       'optimizer': ['Adagrad', 'Adam'],
+       'losses': ['LogCosh'],
        'hidden_layers':[0, 1, 2],
        'batch_size': (20, 50, 5),
        'epochs': [10, 20]}
diff --git a/docs/Examples_Typical.md b/docs/Examples_Typical.md
index 8cbe9e94..e4ffe973 100644
--- a/docs/Examples_Typical.md
+++ b/docs/Examples_Typical.md
@@ -55,8 +55,8 @@ return out, model
 ### Parameter Dictionary
 ```python
 p = {'activation':['relu', 'elu'],
-     'optimizer': ['Nadam', 'Adam'],
-     'losses': ['logcosh'],
+     'optimizer': ['Adagrad', 'Adam'],
+     'losses': ['LogCosh'],
      'hidden_layers':[0, 1, 2],
      'batch_size': (20, 50, 5),
      'epochs': [10, 20]}
diff --git a/docs/Examples_Typical_Code.md b/docs/Examples_Typical_Code.md
index 72bf4214..11a3528a 100644
--- a/docs/Examples_Typical_Code.md
+++ b/docs/Examples_Typical_Code.md
@@ -31,7 +31,7 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
 # set the parameter space boundaries
 p = {'activation':['relu', 'elu'],
-     'optimizer': ['Nadam', 'Adam'],
+     'optimizer': ['Adagrad', 'Adam'],
      'losses': ['categorical_crossentropy'],
      'epochs': [100, 200],
      'batch_size': [4, 6, 8]}
diff --git a/docs/Hidden_Layers.md b/docs/Hidden_Layers.md
index e59b9509..62b65e14 100644
--- a/docs/Hidden_Layers.md
+++ b/docs/Hidden_Layers.md
@@ -18,8 +18,8 @@ When hidden layers are used, `dropout`, `shapes`, `hidden_layers`, and `first_ne
 ```python
 
     p = {'activation':['relu', 'elu'],
-         'optimizer': ['Nadam', 'Adam'],
-         'losses': ['logcosh'],
+         'optimizer': ['Adagrad', 'Adam'],
+         'losses': ['LogCosh'],
          'shapes': ['brick'],          # <<< required
          'first_neuron': [32, 64],     # <<< required
          'hidden_layers':[0, 1, 2],    # <<< required
diff --git a/docs/_coverpage.md b/docs/_coverpage.md
index 0e1635c2..463ba67f 100644
--- a/docs/_coverpage.md
+++ b/docs/_coverpage.md
@@ -1,6 +1,6 @@
 ![logo](_media/talos_logo_bg.png)
 
-## v1.3.3
+## v1.4
 
 > Hyperparameter Experiments with Tensorflow, PyTorch and Keras
 
diff --git a/docs/index.html b/docs/index.html
index 7b917bff..a6f97f90 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -16,7 +16,7 @@
   <div id="app"></div>
   <script>
     window.$docsify = {
-      name: 'Talos 1.3.3',
+      name: 'Talos 1.4',
       repo: 'https://github.com/autonomio/talos',
       coverpage: true,
       loadSidebar: true,
diff --git a/examples/Functional Model Hyperparameter Optimization.ipynb b/examples/Functional Model Hyperparameter Optimization.ipynb
index 465727a6..a0a8a906 100644
--- a/examples/Functional Model Hyperparameter Optimization.ipynb	
+++ b/examples/Functional Model Hyperparameter Optimization.ipynb	
@@ -125,7 +125,7 @@
     "     'epochs': [100],\n",
     "     'dropout': [0],\n",
     "     'kernel_initializer': ['uniform','normal'],\n",
-    "     'optimizer': ['Nadam', 'Adam'],\n",
+    "     'optimizer': ['Adagrad', 'Adam'],\n",
     "     'losses': ['binary_crossentropy'],\n",
     "     'activation':['relu', 'elu'],\n",
     "     'last_activation': ['sigmoid']}"
diff --git a/examples/Hyperparameter Optimization on Keras with Breast Cancer Data.ipynb b/examples/Hyperparameter Optimization on Keras with Breast Cancer Data.ipynb
index 7121b501..f16245dd 100644
--- a/examples/Hyperparameter Optimization on Keras with Breast Cancer Data.ipynb	
+++ b/examples/Hyperparameter Optimization on Keras with Breast Cancer Data.ipynb	
@@ -2,20 +2,21 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "<img src='https://raw.githubusercontent.com/autonomio/hyperio/master/logo.png' width=250px>"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "This notebook is a compliment to the *Hyperparameter Optimization on Keras* article. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Overview\n",
     "\n",
@@ -28,19 +29,20 @@
     "3) Defining the Parameter Space Boundaries \n",
     "\n",
     "4) Running the Experiment"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 1. The Required Inputs and Data"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from tensorflow.keras.models import Sequential\n",
     "from tensorflow.keras.layers import Dropout, Dense\n",
@@ -50,33 +52,33 @@
     "import sys\n",
     "sys.path.insert(0, '/Users/mikko/Documents/GitHub/talos')\n",
     "import talos"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# then we load the dataset\n",
     "x, y = talos.templates.datasets.breast_cancer()\n",
     "\n",
     "# and normalize every feature to mean 0, std 1\n",
     "x = talos.utils.rescale_meanzero(x)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 2. Creating the Keras Model"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# first we have to make sure to input data and params into the function\n",
     "def breast_cancer_model(x_train, y_train, x_val, y_val, params):\n",
@@ -106,20 +108,20 @@
     "                        verbose=0)\n",
     "\n",
     "    return history, model"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 3. Defining the Parameter Space Boundary"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# then we can go ahead and set the parameter space\n",
     "p = {'first_neuron':[9, 10, 11],\n",
@@ -128,24 +130,26 @@
     "     'epochs': [100],\n",
     "     'dropout': [0],\n",
     "     'kernel_initializer': ['uniform','normal'],\n",
-    "     'optimizer': ['Nadam', 'Adam'],\n",
+    "     'optimizer': ['Adagrad', 'Adam'],\n",
     "     'losses': ['binary_crossentropy'],\n",
     "     'activation':['relu', 'elu'],\n",
     "     'last_activation': ['sigmoid']}"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 4. Starting the Experiment"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
    "source": [
     "# and run the experiment\n",
     "t = talos.Scan(x=x,\n",
@@ -155,11 +159,7 @@
     "               experiment_name='breast_cancer',\n",
     "               round_limit=50,\n",
     "               disable_progress_bar=True)"
-   ],
-   "outputs": [],
-   "metadata": {
-    "collapsed": true
-   }
+   ]
   }
  ],
  "metadata": {
@@ -183,4 +183,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/examples/Hyperparameter Optimization with Keras for the Iris Prediction.ipynb b/examples/Hyperparameter Optimization with Keras for the Iris Prediction.ipynb
index 24d22fd1..223c67fb 100644
--- a/examples/Hyperparameter Optimization with Keras for the Iris Prediction.ipynb	
+++ b/examples/Hyperparameter Optimization with Keras for the Iris Prediction.ipynb	
@@ -145,16 +145,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tensorflow.keras.optimizers import Adam, Nadam\n",
+    "from tensorflow.keras.optimizers.legacy import Adam, Adagrad\n",
     "from tensorflow.keras.activations import softmax\n",
-    "from tensorflow.keras.losses import categorical_crossentropy, logcosh\n",
+    "from tensorflow.keras.losses import categorical_crossentropy, LogCosh\n",
     "\n",
     "p = {'lr': (0.1, 10, 10),\n",
     "     'first_neuron':[4, 8, 16, 32, 64, 128],\n",
     "     'batch_size': [2, 3, 4],\n",
     "     'epochs': [200],\n",
     "     'dropout': (0, 0.40, 10),\n",
-    "     'optimizer': [Adam, Nadam],\n",
+    "     'optimizer': [Adam, Adagrad],\n",
     "     'loss': ['categorical_crossentropy'],\n",
     "     'last_activation': ['softmax'],\n",
     "     'weight_regulizer': [None]}"
diff --git a/examples/Recover Best Models from Experiment Log.ipynb b/examples/Recover Best Models from Experiment Log.ipynb
index 44cfd52b..ed4ae3cd 100644
--- a/examples/Recover Best Models from Experiment Log.ipynb	
+++ b/examples/Recover Best Models from Experiment Log.ipynb	
@@ -2,42 +2,44 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "<img src=https://raw.githubusercontent.com/autonomio/hyperio/master/logo.png>"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## How to recover best model from experiment log?\n",
     "Due to system error or other reason where scan_object is no longer available, it's still possible to get best model/s using nothing but the experiment log. In the below notebook you will learn exactly how.\n",
     "\n"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import talos\n",
     "import wrangle\n",
     "from tensorflow.keras.models import Sequential\n",
     "from tensorflow.keras.layers import Dense"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "First we'll have to perform the `Scan()` experiment to produce the experiment log. Because the experiment log is stored on local machine, interrupted `Scan()` or other reason will not affect its availability. The experiment log is updated after each permutation; it contains an up-to-date record of the experiment."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# load the data\n",
     "x, y = talos.templates.datasets.iris()\n",
@@ -45,8 +47,8 @@
     "\n",
     "# set the parameter space boundary\n",
     "p = {'activation':['relu', 'elu'],\n",
-    "     'optimizer': ['Nadam', 'Adam'],\n",
-    "     'losses': ['logcosh'],\n",
+    "     'optimizer': ['Adagrad', 'Adam'],\n",
+    "     'losses': ['LogCosh'],\n",
     "     'shapes': ['brick'],\n",
     "     'first_neuron': [16, 32, 64, 128],\n",
     "     'hidden_layers':[0, 1, 2, 3],\n",
@@ -82,37 +84,37 @@
     "                         experiment_name='minimal_iris',\n",
     "                         params=p,\n",
     "                         round_limit=10)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Now we can assume the case where we no longer have access to the `scan_object`. In this `Scan(...experiment_name...)` was set to \"reactivate\" so we'll find a folder with that name in the present working directory. Next we have to find out what is the name of the experiment log."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# get the name of the experiment log\n",
     "!ls -lhtr minimal_iris"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "What you want to do, is get the name of the `.csv` file you want to use, and use it as part of the input for `experiment_log` in the next step."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from talos.utils.recover_best_model import recover_best_model\n",
     "\n",
@@ -124,41 +126,39 @@
     "                                     input_model=iris_model,\n",
     "                                     n_models=5,\n",
     "                                     task='multi_label')"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Now we can access the cross-validation results:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "results"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We can also access the models and make predictions with them:"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "models[0].predict(x_val)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
@@ -182,4 +182,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..c96e9774
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,55 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "talos"
+dynamic = ["version"]
+description = "Talos Hyperparameter Tuning for Keras"
+readme = "README.md"
+license = "MIT"
+authors = [
+    { name = "Mikko Kotila", email = "mailme@mikkokotila.com" },
+]
+maintainers = [
+    { name = "Mikko Kotila", email = "mailme@mikkokotila.com" },
+]
+classifiers = [
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: MacOS",
+    "Operating System :: Microsoft :: Windows :: Windows 10",
+    "Operating System :: POSIX",
+    "Operating System :: Unix",
+    "Programming Language :: Python :: 2.7",
+    "Programming Language :: Python :: 3.5",
+    "Programming Language :: Python :: 3.6",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Human Machine Interfaces",
+    "Topic :: Scientific/Engineering :: Mathematics",
+]
+dependencies = [
+  "scikit-learn",
+  "statsmodels >= 0.11.0",
+  "tensorflow == 2.14.1",
+  "astetik",
+  "chances",
+  "kerasplotlib",
+  "numpy",
+  "pandas",
+  "requests",
+  "tqdm",
+  "wrangle",
+]
+
+[project.urls]
+Download = "https://github.com/autonomio/talos/"
+Homepage = "http://autonom.io"
+
+[tool.hatch.version]
+path = "talos/__init__.py"
+
+[tool.hatch.build.targets.sdist]
+include = [
+    "/talos",
+]
diff --git a/talos/__init__.py b/talos/__init__.py
index 636a7cc1..cf7da590 100755
--- a/talos/__init__.py
+++ b/talos/__init__.py
@@ -35,4 +35,4 @@
 del commands, scan, model, metrics, key
 del sub, keep_from_templates, template_sub, warnings
 
-__version__ = "1.3.3"
+__version__ = "1.4"
diff --git a/talos/autom8/autoparams.py b/talos/autom8/autoparams.py
index 6cb1e8b2..0e9631b2 100644
--- a/talos/autom8/autoparams.py
+++ b/talos/autom8/autoparams.py
@@ -1,8 +1,8 @@
 import numpy as np
-from tensorflow.keras.optimizers import Adam, Nadam, Adadelta, SGD
+from tensorflow.keras.optimizers.legacy import Adam, Adagrad, SGD
 
 
-loss = {'binary': ['binary_crossentropy', 'logcosh'],
+loss = {'binary': ['binary_crossentropy', 'LogCosh'],
         'multi_class': ['sparse_categorical_crossentropy'],
         'multi_label': ['categorical_crossentropy'],
         'continuous': ['mae']}
@@ -122,7 +122,7 @@ def optimizers(self, optimizers='auto'):
         '''
 
         if optimizers == 'auto':
-            self._append_params('optimizer', [Adam, Nadam, Adadelta, SGD])
+            self._append_params('optimizer', [Adam, Adagrad, SGD])
         else:
             self._append_params('optimizer', optimizers)
 
@@ -168,7 +168,7 @@ def batch_size(self, min_size=8, max_size=None, steps=None):
         integer value at the same time.'''
 
         if max_size is None and steps is None:
-            values = [int(np.exp2(i/2)) for i in range(3, 15)]
+            values = [int(np.exp2(i / 2)) for i in range(3, 15)]
         else:
             values = list(range(min_size, max_size, steps))
 
@@ -180,7 +180,7 @@ def epochs(self, min_epochs=50, max_epochs=None, steps=None):
         integer value at the same time.'''
 
         if max_epochs is None and steps is None:
-            values = [int(np.exp2(i/2))+50 for i in range(3, 15)]
+            values = [int(np.exp2(i / 2)) + 50 for i in range(3, 15)]
         else:
             values = list(range(min_epochs, max_epochs, steps))
 
diff --git a/talos/callbacks/experiment_log.py b/talos/callbacks/experiment_log.py
index 924e7092..f0cd0f28 100644
--- a/talos/callbacks/experiment_log.py
+++ b/talos/callbacks/experiment_log.py
@@ -12,6 +12,7 @@ def __init__(self,
         dictionary from the input model in `Scan()`
 
         experiment_name | str | must match the experiment_name in `Scan()`
+        params | dict | the params dictionary from the input model in `Scan()
 
         '''
 
@@ -22,10 +23,11 @@ def __init__(self,
 
         # get the experiment id first
         list_of_files = glob.glob('./' + experiment_name + '/*.csv')
+
         try:
             latest_file = max(list_of_files, key=os.path.getmtime)
         except ValueError:
-            print("\n TALOS ERROR: `experiment_name` has to match `Scan(experiment_name)`\n")
+            print("\nERROR: `experiment_name` has to match `Scan(experiment_name)`\n")
 
         self.name = latest_file.replace('.csv', '') + '.log'
 
diff --git a/talos/model/normalizers.py b/talos/model/normalizers.py
index 0071f9bd..628b6490 100644
--- a/talos/model/normalizers.py
+++ b/talos/model/normalizers.py
@@ -11,17 +11,15 @@ def lr_normalizer(lr, optimizer):
         The optimizer. For example, Adagrad, Adam, RMSprop.
     """
 
-    from tensorflow.keras.optimizers import SGD, Adam, Adadelta, Adagrad, Adamax, RMSprop
-    from tensorflow.keras.optimizers import Nadam
+    from tensorflow.keras.optimizers.legacy import SGD, Adam, Adagrad, Adamax, RMSprop
+    from tensorflow.keras.optimizers.legacy import Adagrad
     from talos.utils.exceptions import TalosModelError
 
-    if optimizer == Adadelta:
-        pass
-    elif optimizer == SGD or optimizer == Adagrad:
+    if optimizer == SGD or optimizer == Adagrad:
         lr /= 100.0
     elif optimizer == Adam or optimizer == RMSprop:
         lr /= 1000.0
-    elif optimizer == Adamax or optimizer == Nadam:
+    elif optimizer == Adamax:
         lr /= 500.0
     else:
         raise TalosModelError(str(optimizer) + " is not supported by lr_normalizer")
diff --git a/talos/templates/datasets.py b/talos/templates/datasets.py
index 2460eb86..62ced391 100755
--- a/talos/templates/datasets.py
+++ b/talos/templates/datasets.py
@@ -18,9 +18,11 @@ def telco_churn(quantile=.5):
     import wrangle
     import pandas as pd
 
-    df = pd.read_csv('https://raw.githubusercontent.com/autonomio/examples/master/telco_churn/telco_churn_for_sensitivity.csv')
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'examples/master/telco_churn/telco_churn_for_sensitivity.csv'
+    df = pd.read_csv(base_url + url)
 
-    df = df.drop(['val_acc', 'loss', 'f1score', 'acc', 'round_epochs'], 1)
+    df = df.drop(['val_acc', 'loss', 'f1score', 'acc', 'round_epochs'], axis=1)
 
     for col in df.iloc[:, 2:].columns:
         df = wrangle.col_to_multilabel(df, col)
@@ -34,7 +36,7 @@ def telco_churn(quantile=.5):
         y1 = df.C0.values
         y2 = df.C1.values
 
-    x = df.drop(['C0', 'C1'], 1).values
+    x = df.drop(['C0', 'C1'], axis=1).values
 
     return x, [y1, y2]
 
@@ -42,8 +44,10 @@ def telco_churn(quantile=.5):
 def icu_mortality(samples=None):
 
     import pandas as pd
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'icu_mortality.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'icu_mortality.csv')
     df = df.dropna(thresh=3580, axis=1)
     df = df.dropna()
     df = df.sample(frac=1).head(samples)
@@ -56,8 +60,10 @@ def icu_mortality(samples=None):
 def titanic():
 
     import pandas as pd
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'titanic.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'titanic.csv')
 
     y = df.survived.values
 
@@ -81,8 +87,10 @@ def iris():
 
     import pandas as pd
     from tensorflow.keras.utils import to_categorical
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'iris.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'iris.csv')
     df['species'] = df['species'].factorize()[0]
     df = df.sample(len(df))
     y = to_categorical(df['species'])
@@ -98,8 +106,10 @@ def cervical_cancer():
 
     import pandas as pd
     from numpy import nan
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'cervical_cancer.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'cervical_cancer.csv')
     df = df.replace('?', nan)
     df = df.drop(['citology', 'hinselmann', 'biopsy'], axis=1)
     df = df.drop(['since_first_diagnosis',
@@ -116,8 +126,10 @@ def cervical_cancer():
 def breast_cancer():
 
     import pandas as pd
-    base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/'
-    df = pd.read_csv(base + 'breast_cancer.csv')
+
+    base_url = 'https://raw.githubusercontent.com/autonomio/'
+    url = 'datasets/master/autonomio-datasets/'
+    df = pd.read_csv(base_url + url + 'breast_cancer.csv')
 
     # then some minimal data cleanup
     df.drop("Unnamed: 32", axis=1, inplace=True)
diff --git a/talos/templates/models.py b/talos/templates/models.py
index 796f822b..b3552993 100644
--- a/talos/templates/models.py
+++ b/talos/templates/models.py
@@ -103,12 +103,13 @@ def titanic(x_train, y_train, x_val, y_val, params):
                   metrics=['acc'])
 
     # here we are also using the early_stopper function for a callback
-    out = model.fit(x=x_train,
-                    y=y_train,
+    out = model.fit(x=x_train.astype('float32'),
+                    y=y_train.astype('float32'),
                     batch_size=params['batch_size'],
                     epochs=params['epochs'],
                     verbose=0,
-                    validation_data=(x_val, y_val))
+                    validation_data=(x_val.astype('float32'),
+                                     y_val.astype('float32')))
 
     return out, model
 
diff --git a/talos/templates/params.py b/talos/templates/params.py
index aab1d233..8d736838 100644
--- a/talos/templates/params.py
+++ b/talos/templates/params.py
@@ -1,15 +1,15 @@
 def titanic(debug=False):
 
-    from tensorflow.keras.optimizers import Adam, Nadam
+    from tensorflow.keras.optimizers.legacy import Adam, Adagrad
 
     # here use a standard 2d dictionary for inputting the param boundaries
     p = {'lr': (0.5, 5, 10),
          'first_neuron': [4, 8, 16],
          'batch_size': [20, 30, 40],
          'dropout': (0, 0.5, 5),
-         'optimizer': [Adam(), Nadam()],
+         'optimizer': [Adam(), Adagrad()],
          'epochs': [50, 100, 150],
-         'losses': ['logcosh', 'binary_crossentropy'],
+         'losses': ['LogCosh', 'binary_crossentropy'],
          'shapes': ['brick', 'triangle', 0.2],
          'hidden_layers': [0, 1, 2, 3, 4],
          'activation': ['relu', 'elu'],
@@ -18,24 +18,23 @@ def titanic(debug=False):
     if debug:
          
         p = {'lr': [0.1, 0.2],
-            'first_neuron': [4, 8],
-            'batch_size': [20, 30],
-            'dropout': [0.2, 0.3],
-            'optimizer': [Adam(), Nadam()],
-            'epochs': [50, 100],
-            'losses': ['logcosh', 'binary_crossentropy'],
-            'shapes': ['brick', 'triangle', 0.2],
-            'hidden_layers': [0, 1],
-            'activation': ['relu', 'elu'],
-            'last_activation': ['sigmoid']}
+             'first_neuron': [4, 8],
+             'batch_size': [20, 30],
+             'dropout': [0.2, 0.3],
+             'optimizer': [Adam(), Adagrad()],
+             'epochs': [50, 100],
+             'losses': ['LogCosh', 'binary_crossentropy'],
+             'shapes': ['brick', 'triangle', 0.2],
+             'hidden_layers': [0, 1],
+             'activation': ['relu', 'elu'],
+             'last_activation': ['sigmoid']}
 
     return p
 
 
 def iris():
 
-    from tensorflow.keras.optimizers import Adam, Nadam
-    from tensorflow.keras.losses import logcosh, categorical_crossentropy
+    from tensorflow.keras.optimizers.legacy import Adam, Adagrad
     from tensorflow.keras.activations import relu, elu, softmax
 
     # here use a standard 2d dictionary for inputting the param boundaries
@@ -48,8 +47,8 @@ def iris():
          'weight_regulizer': [None],
          'emb_output_dims': [None],
          'shapes': ['brick', 'triangle', 0.2],
-         'optimizer': [Adam, Nadam],
-         'losses': [logcosh, categorical_crossentropy],
+         'optimizer': [Adam, Adagrad],
+         'losses': ['LogCosh', 'categorical_crossentropy'],
          'activation': [relu, elu],
          'last_activation': [softmax]}
 
@@ -58,8 +57,7 @@ def iris():
 
 def breast_cancer():
 
-    from tensorflow.keras.optimizers import Adam, Nadam, RMSprop
-    from tensorflow.keras.losses import logcosh, binary_crossentropy
+    from tensorflow.keras.optimizers.legacy import Adam, Adagrad, RMSprop
     from tensorflow.keras.activations import relu, elu, sigmoid
 
     # then we can go ahead and set the parameter space
@@ -70,8 +68,8 @@ def breast_cancer():
          'epochs': [50, 100, 150],
          'dropout': (0, 0.5, 5),
          'shapes': ['brick', 'triangle', 'funnel'],
-         'optimizer': [Adam, Nadam, RMSprop],
-         'losses': [logcosh, binary_crossentropy],
+         'optimizer': [Adam, Adagrad, RMSprop],
+         'losses': ['LogCosh', 'binary_crossentropy'],
          'activation': [relu, elu],
          'last_activation': [sigmoid]}
 
diff --git a/talos/templates/pipelines.py b/talos/templates/pipelines.py
index 81ba5f42..3aa3605d 100644
--- a/talos/templates/pipelines.py
+++ b/talos/templates/pipelines.py
@@ -44,8 +44,9 @@ def titanic(round_limit=2, random_method='uniform_mersenne', debug=False):
 
     '''Performs a Scan with Iris dataset and simple dense net'''
     import talos as ta
-    scan_object = ta.Scan(ta.templates.datasets.titanic()[0],
-                          ta.templates.datasets.titanic()[1],
+
+    scan_object = ta.Scan(ta.templates.datasets.titanic()[0].astype('float32'),
+                          ta.templates.datasets.titanic()[1].astype('float32'),
                           ta.templates.params.titanic(debug),
                           ta.templates.models.titanic,
                           'test',
diff --git a/talos/utils/recover_best_model.py b/talos/utils/recover_best_model.py
index 8b2fc27f..4f50019a 100644
--- a/talos/utils/recover_best_model.py
+++ b/talos/utils/recover_best_model.py
@@ -53,7 +53,7 @@ def recover_best_model(x_train,
 
         # get the params for the model and train it
         params = df.sort_values(metric, ascending=False)
-        params = params.drop(metric, 1).iloc[i].to_dict()
+        params = params.drop(metric, axis=1).iloc[i].to_dict()
         _history, model = input_model(x_train, y_train, x_val, y_val, params)
 
         # start kfold cross-validation
diff --git a/test-ci.py b/test-ci.py
index b668586d..40eb7076 100755
--- a/test-ci.py
+++ b/test-ci.py
@@ -4,18 +4,13 @@
 
     from tests.commands import *
 
-    test_latest()
-
     scan_object = test_scan()
+    test_latest()
     recover_best_model()
-
     test_random_methods()
-
     test_autom8()
-    test_templates()
-    
-    # test_analyze(scan_object)
-
+    test_templates() 
+    test_analyze(scan_object)
     test_lr_normalizer()
     test_predict()
     test_reducers()
diff --git a/tests/commands/test_analyze.py b/tests/commands/test_analyze.py
index aac1fab9..653a6158 100644
--- a/tests/commands/test_analyze.py
+++ b/tests/commands/test_analyze.py
@@ -5,29 +5,40 @@ def test_analyze(scan_object):
     print('\n >>> Start Analyze()... \n')
 
     import talos
-    import glob
 
     # for now test with old name
     r = talos.Reporting(scan_object)
 
     # read from file
-    list_of_files = glob.glob('./test_latest/' + '/*.csv')
+    #list_of_files = glob.glob('./test_latest/' + '/*.csv')
 
-    r = talos.Reporting(list_of_files[-1])
+    #r = talos.Reporting(list_of_files[-1])
 
     # and then from scan object
     r = talos.Analyze(scan_object)
 
     # test the object properties
     r.best_params('val_loss', ['val_acc'])
-    r.correlate('val_loss', ['val_acc'])
+    r.correlate('val_loss', ['val_acc',
+                             'start',
+                             'end',
+                             'activation',
+                             'optimizer',
+                             'losses',
+                             'shapes'])
     r.data
     r.high('val_acc')
     r.low('val_acc')
 
     # r.plot_bars('first_neuron', 'val_acc', 'dropout', 'hidden_layers')
     r.plot_box('first_neuron', 'val_acc')
-    r.plot_corr('val_loss', ['val_acc'])
+    r.plot_corr('val_loss', ['val_acc',
+                             'start',
+                             'end',
+                             'activation',
+                             'optimizer',
+                             'losses',
+                             'shapes'])
     r.plot_hist('val_acc')
     r.plot_kde('val_acc')
     r.plot_line('val_acc')
diff --git a/tests/commands/test_autom8.py b/tests/commands/test_autom8.py
index 9f390efe..c5a56104 100644
--- a/tests/commands/test_autom8.py
+++ b/tests/commands/test_autom8.py
@@ -3,7 +3,7 @@ def test_autom8():
     import talos
     import wrangle
 
-    from tensorflow.keras.optimizers import Adam
+    from tensorflow.keras.optimizers.legacy import Adam
 
     print('\n >>> start AutoParams()... \n')
 
diff --git a/tests/commands/test_latest.py b/tests/commands/test_latest.py
index 89933add..b1a690ee 100644
--- a/tests/commands/test_latest.py
+++ b/tests/commands/test_latest.py
@@ -13,8 +13,8 @@ def test_latest():
     x, y = talos.templates.datasets.iris()
 
     p = {'activation': ['relu', 'elu'],
-         'optimizer': ['Nadam', 'Adam'],
-         'losses': ['logcosh'],
+         'optimizer': ['Adagrad', 'Adam'],
+         'losses': ['LogCosh'],
          'shapes': ['brick'],
          'first_neuron': [16, 32, 64, 128],
          'hidden_layers': [0, 1, 2, 3],
diff --git a/tests/commands/test_rest.py b/tests/commands/test_rest.py
index 3ab515f5..7382e7e5 100644
--- a/tests/commands/test_rest.py
+++ b/tests/commands/test_rest.py
@@ -21,8 +21,7 @@ def test_rest(scan_object):
     x_train, y_train, x_val, y_val = talos.utils.val_split(x, y, .2)
     x = talos.utils.rescale_meanzero(x)
 
-    callbacks = [
-                 talos.utils.early_stopper(10),
+    callbacks = [talos.utils.early_stopper(10),
                  talos.callbacks.ExperimentLog('test', {})]
 
     metrics = [talos.utils.metrics.f1score,
@@ -46,7 +45,7 @@ def test_rest(scan_object):
     model1 = Sequential()
     model1.add(Dense(10, input_dim=x.shape[1]))
     model1.add(Dense(1))
-    model1.compile('adam', 'logcosh', metrics=metrics)
+    model1.compile('adam', 'LogCosh', metrics=metrics)
     model1.fit(x, y, callbacks=callbacks)
 
     print('\n ...generator... \n')
@@ -54,7 +53,7 @@ def test_rest(scan_object):
     model2 = Sequential()
     model2.add(Dense(10, input_dim=x.shape[1]))
     model2.add(Dense(1))
-    model2.compile('adam', 'logcosh')
+    model2.compile('adam', 'LogCosh')
     model2.fit_generator(talos.utils.generator(x, y, 10), 5)
 
     print('\n ...SequenceGenerator... \n')
@@ -62,7 +61,7 @@ def test_rest(scan_object):
     model3 = Sequential()
     model3.add(Dense(10, input_dim=x.shape[1]))
     model3.add(Dense(1))
-    model3.compile('adam', 'logcosh')
+    model3.compile('adam', 'LogCosh')
     model3.fit_generator(talos.utils.SequenceGenerator(x, y, 10))
 
     print('\n ...gpu_utils... \n')
@@ -75,6 +74,4 @@ def test_rest(scan_object):
     from talos.utils.test_utils import create_param_space
     create_param_space(restored.results, 8)
 
-
-
     print('finished testing the rest \n')
diff --git a/tests/commands/test_scan.py b/tests/commands/test_scan.py
index 8ae91b02..1916694a 100644
--- a/tests/commands/test_scan.py
+++ b/tests/commands/test_scan.py
@@ -3,16 +3,17 @@ def test_scan():
     print("\n >>> start Scan()...")
 
     import talos
+    import tensorflow as tf
 
     from tensorflow.keras.losses import binary_crossentropy
-    from tensorflow.keras.optimizers import Adam, Nadam
+    from tensorflow.keras.optimizers.legacy import Adam
     from tensorflow.keras.activations import relu, elu
     from tensorflow.keras.layers import Dense
     from tensorflow.keras.models import Sequential
 
     p = {'activation': [relu, elu],
-         'optimizer': ['Nadam', Adam],
-         'losses': ['logcosh', binary_crossentropy],
+         'optimizer': ['Adagrad', Adam],
+         'losses': ['LogCosh', binary_crossentropy],
          'shapes': ['brick', 'funnel', 'triangle'],
          'first_neuron': [16],
          'hidden_layers': ([0, 1, 2, 3]),
@@ -49,12 +50,12 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
     x, y = talos.templates.datasets.iris()
 
-    p_for_q = {'activation':['relu', 'elu'],
-               'optimizer': ['Nadam', 'Adam'],
-               'losses': ['logcosh'],
+    p_for_q = {'activation': ['relu', 'elu'],
+               'optimizer': ['Adagrad', 'Adam'],
+               'losses': ['LogCosh'],
                'shapes': ['brick'],
                'first_neuron': [16, 32, 64, 128],
-               'hidden_layers':[0, 1, 2, 3],
+               'hidden_layers': [0, 1, 2, 3],
                'dropout': [.2, .3, .4],
                'batch_size': [20, 30, 40, 50],
                'epochs': [10]}
@@ -74,7 +75,6 @@ def iris_model(x_train, y_train, x_val, y_val, params):
                              reduction_metric='val_acc',
                              minimize_loss=False)
 
-
     x = x[:50]
     y = y[:50]
 
@@ -137,8 +137,8 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
     # the create the test based on it
 
-    _keras_model = scan_object.best_model()
-    _keras_model = scan_object.best_model('loss', True)
+    scan_object.best_model()
+    scan_object.best_model('loss', True)
 
     scan_object.evaluate_models(x_val=scan_object.x,
                                 y_val=scan_object.y,
@@ -155,7 +155,4 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
     print('finised Scan() object \n')
 
-    # # # # # # # # # # # # # # # # # #
-
-
     return scan_object
diff --git a/tests/commands/test_templates.py b/tests/commands/test_templates.py
index af647086..f1339f78 100644
--- a/tests/commands/test_templates.py
+++ b/tests/commands/test_templates.py
@@ -12,6 +12,7 @@ def test_templates():
     talos.Scan(x, y, p, model, 'test_', round_limit=2)
 
     x, y = talos.templates.datasets.iris()
+
     x = x[:50]
     y = y[:50]
     model = talos.templates.models.iris
diff --git a/tests/performance/memory_pressure.py b/tests/performance/memory_pressure.py
index 8630f415..adebc71b 100644
--- a/tests/performance/memory_pressure.py
+++ b/tests/performance/memory_pressure.py
@@ -6,7 +6,7 @@
 from tensorflow.keras.layers import Conv2D
 
 p = {'activation': ['relu'],
-     'optimizer': ['AdaDelta'],
+     'optimizer': ['Adam'],
      'losses': ['categorical_crossentropy'],
      'dropout': [.2],
      'batch_size': [256],