diff --git a/Project/simple_approach/Continous_Learner.ipynb b/Project/simple_approach/Continous_Learner.ipynb
new file mode 100644
index 0000000..49c586a
--- /dev/null
+++ b/Project/simple_approach/Continous_Learner.ipynb
@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Continous Learner for Emoji classifier 🤓\n",
+    "**usage:**\n",
+    "run all cells, then go to the [user interface](#User-Interface)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     /home/jonas/nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n",
+      "[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import simple_twitter_learning as stl\n",
+    "import glob\n",
+    "import sys\n",
+    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## user interface area:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* UI helper functions and global states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import clear_output, Markdown, Math\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "out_areas = {}\n",
+    "shown_widgets = {}\n",
+    "tab_manager = widgets.Tab()\n",
+    "\n",
+    "def mp(obj):\n",
+    "    display(Markdown(obj))\n",
+    "\n",
+    "def set_widget_visibility(widget_names, visible=True):\n",
+    "    for w in widget_names:\n",
+    "        shown_widgets[w].disabled = not visible\n",
+    "\n",
+    "def create_area(area_name:str, list_widgets:list, out_name:str, tab=tab_manager):\n",
+    "    \"\"\"\n",
+    "    creates a table of widgets with corresponding output area below\n",
+    "    \n",
+    "    @param area_name: title of the area\n",
+    "    @param list_widgets: list of tuples: (widget, name:str)\n",
+    "    @param out_name: name for the output area\n",
+    "    \"\"\"\n",
+    "    if out_name is not None:\n",
+    "        out = widgets.Output()\n",
+    "        out_areas[out_name] = out\n",
+    "    h_box_widgets = []\n",
+    "    v_box_widgets = []\n",
+    "    for v in list_widgets:\n",
+    "        for h in v:\n",
+    "            if 'description' in h[0].__dir__():\n",
+    "                h[0].description = h[1]\n",
+    "            shown_widgets[h[1]] = h[0]\n",
+    "            h_box_widgets.append(h[0])\n",
+    "        v_box_widgets.append(widgets.HBox(h_box_widgets))\n",
+    "        h_box_widgets = []\n",
+    "    \n",
+    "    if out_name is not None:\n",
+    "        v_box_widgets += [out]\n",
+    "    tab.children = list(tab.children) + [widgets.VBox(v_box_widgets)]\n",
+    "    tab.set_title(len(tab.children) - 1, area_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* build UI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "----"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/markdown": [
+       "## User Interface"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c11801d12b643d9b059ba1058d66d5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Tab(children=(VBox(children=(HBox(children=(Text(value='./data_en/', description='root_path'), Button(descript…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mp(\"----\")\n",
+    "mp(\"## User Interface\")\n",
+    "# create widgets\n",
+    "create_area(\"load dataset 💾\",\n",
+    "           [\n",
+    "               [\n",
+    "                   (widgets.Text(value=\"./data_en/\"), \"root_path\"),\n",
+    "                   (widgets.Button(), \"set_path\")\n",
+    "               ],\n",
+    "               [\n",
+    "                   (widgets.IntRangeSlider(disabled=True, min=0, max=0), \"file_range\"),\n",
+    "                   (widgets.Checkbox(disabled=True), \"only_emoticons\")\n",
+    "               ],\n",
+    "               [\n",
+    "                   (widgets.BoundedIntText(disabled=True,min=-1, max=10), \"k_means_cluster\"),\n",
+    "                   (widgets.BoundedIntText(disabled=True,min=-1, max=10), \"n_top_emojis\")\n",
+    "               ],\n",
+    "               [\n",
+    "                   (widgets.Button(disabled=True),\"load_data\")\n",
+    "               ]\n",
+    "           ],\n",
+    "           \"load\")\n",
+    "\n",
+    "classifier_tab = widgets.Tab()\n",
+    "\n",
+    "create_area(\"keras\",\n",
+    "           [\n",
+    "               [\n",
+    "                   (widgets.IntSlider(min=0, max=10), \"n_keras_layer\")\n",
+    "               ],\n",
+    "               [\n",
+    "                   (widgets.HBox([]), \"n_keras_neurons\")\n",
+    "               ],\n",
+    "               [\n",
+    "                   (widgets.HBox([]), \"keras_funcs\")\n",
+    "               ]\n",
+    "           ],\n",
+    "           None,\n",
+    "           classifier_tab)\n",
+    "\n",
+    "create_area(\"create classifier\",\n",
+    "           [\n",
+    "               [\n",
+    "                   (classifier_tab, \"classifier_tab\")\n",
+    "               ],\n",
+    "               [\n",
+    "                   (widgets.Button(), \"create_classifier\")\n",
+    "               ],\n",
+    "               [\n",
+    "                   (widgets.Text(), \"classifier name\"),\n",
+    "                   (widgets.Button(), \"save classifier\")\n",
+    "               ]\n",
+    "           ],\n",
+    "           \"create\")\n",
+    "\n",
+    "create_area(\"train classifier 🎓\", \n",
+    "            [\n",
+    "                [\n",
+    "                    (widgets.IntSlider(value=0,min=0,max=0), \"batch_size\"),\n",
+    "                    (widgets.FloatSlider(value=0.15, min=0, max=1), \"val_split\"),\n",
+    "                    (widgets.IntText(value=1), \"n_epochs\")\n",
+    "                ],\n",
+    "                [\n",
+    "                    (widgets.Button(),\"train\")\n",
+    "                ]\n",
+    "            ], \n",
+    "            \"train\" )\n",
+    "tab_manager"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## global variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdm = None\n",
+    "pm = None\n",
+    "tr = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## pretty jupyter print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections\n",
+    "import traceback\n",
+    "from pprint import pprint as pp\n",
+    "\n",
+    "def jupyter_print(obj, cell_w = 10, headers=None, p_type=True, ret_mdown=False, index_offset=0, list_horizontal=False):\n",
+    "    \"\"\"\n",
+    "    pretty hacky function to convert arrays, lists and matrices into\n",
+    "    nice readable markdown code and render that in jupyter. if that is not possible\n",
+    "    it will use pretty print instead\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        ts = \"**Type:** \" + str(type(obj)).strip(\"<>\") + \"\\n\\n\"\n",
+    "        if type(obj) == str:\n",
+    "            display(Markdown(obj))\n",
+    "        elif isinstance(obj, collections.Iterable):\n",
+    "            if isinstance(obj[0], collections.Iterable) and type(obj[0]) is not str:\n",
+    "                # we have a table\n",
+    "                \n",
+    "                if headers is None:\n",
+    "                    headers = [str(i) for i in range(len(obj[0]))]\n",
+    "                \n",
+    "                if len(headers) < len(obj[0]):\n",
+    "                    headers += [\" \" for i in range(len(obj[0]) - len(headers))]\n",
+    "                \n",
+    "                s = \"|\" + \" \" * cell_w + \"|\"\n",
+    "                \n",
+    "                for h in headers:\n",
+    "                    s += str(h) + \" \" * (cell_w - len(h)) + \"|\"\n",
+    "                s += \"\\n|\" + \"-\" * (len(headers) + (len(headers) + 1) * cell_w) + \"|\\n\"\n",
+    "                \n",
+    "                #s = (\"|\" + (\" \" * (cell_w))) * len(obj[0]) + \"|\\n\" + \"|\" + (\"-\" * (cell_w + 1)) * len(obj[0])\n",
+    "                #s += '|\\n'\n",
+    "                \n",
+    "                row = index_offset\n",
+    "                \n",
+    "                for o in obj:\n",
+    "                    s += \"|**\" + str(row) + \"**\" + \" \" * (cell_w - (len(str(row))+4))\n",
+    "                    row += 1\n",
+    "                    for i in o:\n",
+    "                        s += \"|\" + str(i) + \" \" * (cell_w - len(str(i)))\n",
+    "                    s+=\"|\" + '\\n'\n",
+    "                s += ts\n",
+    "                display(Markdown(s))\n",
+    "                return s if ret_mdown else None\n",
+    "            else:\n",
+    "                # we have a list\n",
+    "                \n",
+    "            \n",
+    "                if headers is None:\n",
+    "                    headers = [\"index\",\"value\"]\n",
+    "                    \n",
+    "                index_title = headers[0]\n",
+    "                value_title = headers[1]\n",
+    "                \n",
+    "                s = \"|\" + index_title + \" \" * (cell_w - len(value_title)) + \"|\" + value_title + \" \" * (cell_w - len(value_title)) + \"|\" + '\\n'\n",
+    "                s += \"|\" + \"-\" * (1 + 2 * cell_w) + '|\\n'\n",
+    "                i = index_offset\n",
+    "                for o in obj:\n",
+    "                    s_i = str(i)\n",
+    "                    s_o = str(o)\n",
+    "                    s += \"|\" + s_i + \" \" * (cell_w - len(s_i)) + \"|\" + s_o + \" \" * (cell_w - len(s_o)) + \"|\" + '\\n'\n",
+    "                    i+=1\n",
+    "                s += ts\n",
+    "                #print(s)\n",
+    "                display(Markdown(s))\n",
+    "                return s if ret_mdown else None\n",
+    "        else:\n",
+    "            jupyter_print([obj])\n",
+    "    except Exception as e:\n",
+    "        print(ts)\n",
+    "        pp(obj) \n",
+    "\n",
+    "jp = jupyter_print"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## output progress printing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class progress_indicator(object):\n",
+    "    \n",
+    "    def __init__(self, n, description=\"progress\"):\n",
+    "        self.w = widgets.IntProgress(value=0, min=0,max=n, description = description)\n",
+    "        display(self.w)\n",
+    "    def update(self, dn=1):\n",
+    "        self.w.value += dn\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "## load datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def set_path(b):\n",
+    "    with out_areas[\"load\"]:\n",
+    "        clear_output()\n",
+    "        mp(\"----\")\n",
+    "        files = sorted(glob.glob(shown_widgets[\"root_path\"].value + \"/*.json\"))\n",
+    "        \n",
+    "        if len(files) == 0:\n",
+    "            sys.stderr.write(\"ERROR: no json files available in \" + shown_widgets[\"root_path\"].value + \"\\n\")\n",
+    "            set_widget_visibility([\"file_range\",\n",
+    "                               \"only_emoticons\",\n",
+    "                               \"k_means_cluster\",\n",
+    "                               \"n_top_emojis\",\n",
+    "                               \"load_data\"], False)\n",
+    "            return\n",
+    "            \n",
+    "        mp(\"**available files:**\")\n",
+    "        jp(files, headers=[\"fileindex\",\"filepath\"])\n",
+    "        set_widget_visibility([\"file_range\",\n",
+    "                               \"only_emoticons\",\n",
+    "                               \"k_means_cluster\",\n",
+    "                               \"n_top_emojis\",\n",
+    "                               \"load_data\"], True)\n",
+    "        shown_widgets[\"file_range\"].min=0\n",
+    "        shown_widgets[\"file_range\"].max=len(files) -1\n",
+    "\n",
+    "def load_data(b):\n",
+    "    global sdm\n",
+    "    with out_areas[\"load\"]:\n",
+    "        clear_output()\n",
+    "        mp(\"----\")\n",
+    "        \n",
+    "        r = shown_widgets[\"file_range\"].value\n",
+    "        r = (r[0], r[1] + 1) # range has to be exclusive according to the last element!\n",
+    "        \n",
+    "        p = progress_indicator(r[1] - r[0], \"reading progress\")\n",
+    "        \n",
+    "        sdm = stl.sample_data_manager.generate_and_read(path=shown_widgets[\"root_path\"].value,\n",
+    "                                                    n_top_emojis=shown_widgets[\"n_top_emojis\"].value,\n",
+    "                                                    file_range=range(r[0], r[1]),\n",
+    "                                                    n_kmeans_cluster=shown_widgets[\"k_means_cluster\"].value,\n",
+    "                                                    progress_callback=p.update)\n",
+    "        shown_widgets[\"batch_size\"].max = len(sdm.labels)\n",
+    "        \n",
+    "        \n",
+    "# linking functions with buttons:\n",
+    "shown_widgets[\"set_path\"].on_click(set_path)\n",
+    "shown_widgets[\"load_data\"].on_click(load_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(b):\n",
+    "    global sdm\n",
+    "    global pm\n",
+    "    global tr\n",
+    "    with out_areas[\"train\"]:\n",
+    "        clear_output()\n",
+    "        mp(\"----\")\n",
+    "        if sdm is None or pm is None:\n",
+    "            sys.stderr.write(\"ERROR: sample data and/or classifier missing!\\n\")\n",
+    "            return\n",
+    "    \n",
+    "        batch_size = shown_widgets[\"batch_size\"].value\n",
+    "        val_split = shown_widgets[\"val_split\"].value\n",
+    "        n_epochs = shown_widgets[\"n_epochs\"].value\n",
+    "        \n",
+    "        print(\"update train test split:\")\n",
+    "        sdm.create_train_test_split(split=val_split)\n",
+    "        batch_n = len(sdm.X) // batch_size\n",
+    "        \n",
+    "        print(\"fit\")\n",
+    "        \n",
+    "        p = progress_indicator(batch_n)\n",
+    "        \n",
+    "        tr = stl.trainer(sdm=sdm, pm=pm)\n",
+    "        tr.fit(progress_callback=p.update, batch_size=batch_size, n_epochs=n_epochs)\n",
+    "        \n",
+    "\n",
+    "# linking:\n",
+    "shown_widgets[\"train\"].on_click(train)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## create classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "keras_acivations = [\n",
+    "    \"softmax\",\n",
+    "    \"elu\",\n",
+    "    \"selu\",\n",
+    "    \"softplus\",\n",
+    "    \"softsign\",\n",
+    "    \"relu\",\n",
+    "    \"tanh\",\n",
+    "    \"sigmoid\",\n",
+    "    \"hard_sigmoid\",\n",
+    "    \"linear\",\n",
+    "    \"None\"\n",
+    "]\n",
+    "\n",
+    "def populate_keras_options(b):\n",
+    "    n_layers = shown_widgets[\"n_keras_layer\"].value\n",
+    "    hbox_neurons = shown_widgets[\"n_keras_neurons\"]\n",
+    "    hbox_funcs = shown_widgets[\"keras_funcs\"]\n",
+    "    \n",
+    "    hbox_neurons.children = [widgets.IntText(description = str(i)) for i in range(n_layers)]\n",
+    "    hbox_funcs.children = [widgets.Dropdown(options=keras_acivations,description = str(i)) for i in range(n_layers)]\n",
+    "    \n",
+    "    #hbox_neurons.children[-1].disabled = True\n",
+    "\n",
+    "def create_classifier(b):\n",
+    "    global sdm\n",
+    "    global pm\n",
+    "    global tr\n",
+    "    with out_areas[\"create\"]:\n",
+    "        clear_output()\n",
+    "        mp(\"----\")\n",
+    "        if sdm is None:\n",
+    "            sys.stderr.write(\"load a dataset first!\\n\")\n",
+    "            return\n",
+    "        \n",
+    "        chosen_classifier = classifier_tab.get_title(classifier_tab.selected_index)\n",
+    "        \n",
+    "        mp(\"**chosen classifier**: `\" + chosen_classifier + \"`\")\n",
+    "        \n",
+    "        # TODO: add more classifier options here:\n",
+    "        if chosen_classifier is 'keras':\n",
+    "            sdm.create_train_test_split()\n",
+    "            \n",
+    "            n_layers = shown_widgets[\"n_keras_layer\"].value\n",
+    "            hbox_neurons = shown_widgets[\"n_keras_neurons\"]\n",
+    "            hbox_funcs = shown_widgets[\"keras_funcs\"]\n",
+    "\n",
+    "            layers = []\n",
+    "            for i in range(n_layers):\n",
+    "                func = hbox_funcs.children[i].value\n",
+    "                if func == 'None':\n",
+    "                    func = None\n",
+    "                layers.append((hbox_neurons.children[i].value, func))\n",
+    "            \n",
+    "            # modify last layer:\n",
+    "            layers[-1] = (sdm.y.shape[1], layers[-1][1])\n",
+    "            \n",
+    "            mp(\"**layers:** \")\n",
+    "            jp(layers, headers=['#neurons', 'activation_func'])\n",
+    "\n",
+    "            pm = stl.pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer=TfidfVectorizer(stop_words='english'),\n",
+    "                                                           layers=layers, sdm=sdm)\n",
+    "\n",
+    "# link\n",
+    "shown_widgets[\"n_keras_layer\"].observe(populate_keras_options)\n",
+    "shown_widgets[\"create_classifier\"].on_click(create_classifier)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Project/simple_approach/simple_twitter_learning.py b/Project/simple_approach/simple_twitter_learning.py
index 8d13eee..5f1cf26 100644
--- a/Project/simple_approach/simple_twitter_learning.py
+++ b/Project/simple_approach/simple_twitter_learning.py
@@ -122,7 +122,7 @@ def get_wordnet_pos(treebank_tag):
 
 class sample_data_manager(object):
     @staticmethod
-    def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1):
+    def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1, progress_callback=None):
         """
         generate, read and process train data in one step.
         
@@ -136,7 +136,7 @@ class sample_data_manager(object):
         @return: sample_data_manager object
         """
         sdm = sample_data_manager(path)
-        sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons)
+        sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons, progress_callback=progress_callback)
         if apply_stemming:
             sdm.apply_stemming_and_lemmatization()
         
@@ -176,7 +176,7 @@ class sample_data_manager(object):
         self.kmeans_cluster = None
         self.label_binarizer = None
     
-    def read_files(self, file_index_range:list, only_emoticons=True):
+    def read_files(self, file_index_range:list, only_emoticons=True, progress_callback=None):
         """
         reading (multiple) files to one panda table.
         
@@ -190,7 +190,8 @@ class sample_data_manager(object):
                 self.raw_data = pd.read_json(self.json_files[i], encoding="utf-8")
             else:
                 self.raw_data = self.raw_data.append(pd.read_json(self.json_files[i], encoding="utf-8"))
-        
+            if progress_callback is not None:
+                progress_callback()
         self.emojis = self.raw_data['EMOJI']
         self.plain_text = self.raw_data['text']
         
@@ -500,7 +501,7 @@ class trainer(object):
         self.sdm = sdm
         self.pm = pm
     
-    def fit(self, max_size=10000, disabled_fit_steps=['vectorizer']):
+    def fit(self, max_size=10000, disabled_fit_steps=['vectorizer'], keras_batch_fitting_layer=['keras_model'], batch_size=None, n_epochs=1, progress_callback=None):
         """
         fitting data in the pipeline. Because we don't want to refit the vectorizer, the pipeline models containing the vectorizer have to be named explicitly
         
@@ -513,22 +514,44 @@ class trainer(object):
         disabled_fits = {}
         disabled_fit_transforms = {}
         
+        disabled_keras_fits = {}
+        
         named_steps = self.pm.pipeline.named_steps
         
         for s in disabled_fit_steps:
-            # now it gets a little bit dirty:
+            # now it gets really dirty:
             # replace fit functions we don't want to call again (e.g. for vectorizers)
             disabled_fits[s] = named_steps[s].fit
             disabled_fit_transforms[s] = named_steps[s].fit_transform
             named_steps[s].fit = lambda self, X, y=None: self
             named_steps[s].fit_transform = named_steps[s].transform
-            
-        self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])
+        
+        for k in keras_batch_fitting_layer:
+            # forcing batch fitting on keras
+            disabled_keras_fits[k]=named_steps[k].fit
+            named_steps[k].fit = lambda X, y: named_steps[k].train_on_batch(X.todense(), y) # ← why has keras no sparse support on batch progressing!?!?!
+        
+        if batch_size is None:
+            self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])
+        else:
+            n = len(self.sdm.X) // batch_size
+            for i in range(n_epochs):
+                for j in range(n):
+                    self.pm.fit(X = np.array(self.sdm.X[j*batch_size:(j+1)*batch_size]), y = np.array(self.sdm.y[j*batch_size:(j+1)*batch_size]))
+                    if progress_callback is not None:
+                        progress_callback()
+                    pred, yt = self.test()
+                    mean_squared_error = ((pred - yt)**2).mean(axis=0)
+                    print("#" + str(j) + ": loss: ", mean_squared_error)
+
         
         # restore replaced fit functions:
         for s in disabled_fit_steps:
             named_steps[s].fit = disabled_fits[s]
             named_steps[s].fit_transform = disabled_fit_transforms[s]
+        
+        for k in keras_batch_fitting_layer:
+            named_steps[k].fit = disabled_keras_fits[k]
     
     def test(self):
         '''