--- a
+++ b/GP/MLP/mlp.py
@@ -0,0 +1,122 @@
+from scipy.stats.stats import pearsonr
+import pandas as pd
+import numpy as np
+from keras import backend as K
+from keras.models import Sequential
+from keras.layers import Dense, Activation
+from keras.layers import Dropout
+from keras import regularizers
+from keras.activations import relu, elu, linear, softmax, tanh, softplus
+from keras.callbacks import EarlyStopping, Callback
+from keras.wrappers.scikit_learn import KerasRegressor
+from keras.optimizers import Adam, Nadam, sgd,Adadelta, RMSprop
+from keras.losses import mean_squared_error, categorical_crossentropy, logcosh
+from keras.utils.np_utils import to_categorical
+from keras import metrics
+
+
+from keras import regularizers
+#keras Load Model
+from keras.models import load_model
+
+import talos as ta
+import wrangle as wr
+from talos.metrics.keras_metrics import fmeasure_acc
+from talos.model.layers import hidden_layers
+from talos import live
+from talos.model import lr_normalizer, early_stopper, hidden_layers
+import os
+
+from CNN.cnn import acc_pearson_r  as acc_pearson_r
+
+def mlp_main(x, y, x_val, y_val, params):
+
+    model_mlp = Sequential()
+    nSNP = x.shape[1]
+    try:
+        out_c= y.shape[1]
+    except IndexError:
+        out_c=1
+
+
+    model_mlp.add(Dense(params['first_neuron'], input_dim=nSNP,
+                        activation=params['activation'],
+                        kernel_initializer='normal', kernel_regularizer=regularizers.l2(params['reg1'])))
+
+    model_mlp.add(Dropout(params['dropout_1']))
+    if (params['hidden_layers'] != 0):
+        # if we want to also test for number of layers and shapes, that's possible
+        for _ in range(params['hidden_layers']):
+            model_mlp.add(Dense(params['hidden_neurons'], activation=params['activation'],
+                                kernel_regularizer=regularizers.l2(params['reg1'])))
+
+            # hidden_layers(model, params, 1)
+            model_mlp.add(Dropout(params['dropout_2']))
+
+    model_mlp.add(Dense(out_c,activation=params['last_activation'],
+    kernel_regularizer=regularizers.l2(params['reg2'])))
+    if params['optimizer']=='Adam':
+        params['optimizer']= Adam
+    if params['optimizer']=='Nadam':
+        params['optimizer']= Nadam
+    if params['optimizer']=='sgd':
+        params['optimizer']= sgd
+
+    model_mlp.compile(loss=mean_squared_error,
+                     optimizer=params['optimizer'](lr=lr_normalizer(params['lr'], params['optimizer'])),
+                     metrics=[acc_pearson_r])
+    #es = EarlyStopping(monitor=mean_squared_error, mode='min', verbose=1)
+
+    # callbacks=[live()] see the output
+    # callbacks= es to EarlyStopping
+
+    out_mlp = model_mlp.fit(x, y, validation_split=0.2,
+                            verbose=0, batch_size=params['batch_size'],
+                            epochs=params['epochs'])
+
+    return out_mlp, model_mlp
+
+def mlp_main_cat(x, y, x_val, y_val, params):
+
+    model_mlp = Sequential()
+    nSNP = x.shape[1]
+    last_layer= y.shape[1]
+
+    model_mlp.add(Dense(params['first_neuron'], input_dim=nSNP,
+                        activation=params['activation'],
+                        kernel_initializer='normal', activity_regularizer=regularizers.l1(params['reg1'])))
+
+    model_mlp.add(Dropout(params['dropout_1']))
+    if (params['hidden_layers'] != 0):
+        # if we want to also test for number of layers and shapes, that's possible
+        for _ in range(params['hidden_layers']):
+            model_mlp.add(Dense(params['hidden_neurons'], activation=params['activation'],
+                                activity_regularizer=regularizers.l2(params['reg1'])))
+
+            # hidden_layers(model, params, 1)
+            model_mlp.add(Dropout(params['dropout_2']))
+    model_mlp.add(Dense(last_layer,activation='softmax'))
+    if params['optimizer']=='Adam':
+        params['optimizer']= Adam
+    if params['optimizer']=='Nadam':
+        params['optimizer']= Nadam
+    if params['optimizer']=='sgd':
+        params['optimizer']= sgd
+
+    model_mlp.compile(loss='categorical_crossentropy',optimizer=params['optimizer'](lr=lr_normalizer(params['lr'],
+    params['optimizer'])), metrics=['accuracy'])
+
+
+    #acc or mean_squared_error in metrics
+    # simple early stopping
+    # if you monitor is an accuracy parameter (pearson here, you should chose mode="max"), otherwise it would be "min"
+    #es = EarlyStopping(monitor=mean_squared_error, mode='min', verbose=1)
+
+    # callbacks=[live()] see the output
+    # callbacks= es to EarlyStopping
+
+    out_mlp = model_mlp.fit(x, y, validation_split=0.2,
+                            verbose=0, batch_size=params['batch_size'],
+                            epochs=params['epochs'])
+
+    return out_mlp, model_mlp