diff --git a/gym/envs/parameter_tuning/train_deep_cnn.py b/gym/envs/parameter_tuning/train_deep_cnn.py
index 13e1e5819..8d3359359 100644
--- a/gym/envs/parameter_tuning/train_deep_cnn.py
+++ b/gym/envs/parameter_tuning/train_deep_cnn.py
@@ -293,3 +293,108 @@ class CNNClassifierTraining(gym.Env):
         self.epoch_idx = 0
         
         return self._get_obs()
+
+def train_blueprint(self, lr, decay, momentum, batch_size, l1, l2, convs, fcs):
+
+        X, Y, Xv, Yv = self.data
+        nb_classes = self.nb_classes
+
+        reg = WeightRegularizer()
+
+        # a hack to make regularization variable
+        reg.l1 = K.variable(0.0)
+        reg.l2 = K.variable(0.0)
+
+        # input square image dimensions
+        img_rows, img_cols = X.shape[-1], X.shape[-1]
+        img_channels = X.shape[1]
+
+        # convert class vectors to binary class matrices
+        Y = np_utils.to_categorical(Y, nb_classes)
+        Yv = np_utils.to_categorical(Yv, nb_classes)
+
+        # here definition of the model happens
+        model = Sequential()
+
+        has_convs = False
+        # create all convolutional layers
+        for val, use in convs.reshape((5, 2)):
+
+            # Size of convolutional layer
+            cnvSz = int(val * 128)+1
+
+            if use < 0.5:
+                continue
+            has_convs = True
+            model.add(Convolution2D(cnvSz, 3, 3, border_mode='same',
+                                    input_shape=(img_channels, img_rows, img_cols),
+                                    W_regularizer=reg,
+                                    b_regularizer=reg))
+            model.add(Activation('relu'))
+
+            model.add(MaxPooling2D(pool_size=(2, 2)))
+            # model.add(Dropout(0.25))
+
+        if has_convs:
+            model.add(Flatten())
+        else:
+            model.add(Flatten( input_shape=(img_channels, img_rows, img_cols) )) # avoid excetpions on no convs
+
+        # create all fully connected layers
+        for val, use in fcs.reshape((2, 2)):
+
+            if use < 0.5:
+                continue
+
+            # choose fully connected layer size
+            densesz = int(1024 * val)+1
+
+            model.add(Dense(densesz,
+                            W_regularizer=reg,
+                            b_regularizer=reg))
+            model.add(Activation('relu'))
+            # model.add(Dropout(0.5))
+
+        model.add(Dense(nb_classes,
+                        W_regularizer=reg,
+                        b_regularizer=reg))
+        model.add(Activation('softmax'))
+
+        # let's train the model using SGD + momentum (how original).
+        sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+        model.compile(loss='categorical_crossentropy',
+                      optimizer=sgd,
+                      metrics=['accuracy'])
+
+        X = X.astype('float32')
+        Xv = Xv.astype('float32')
+        X /= 255
+        Xv /= 255
+
+        model = model
+        sgd = sgd
+        reg = reg
+
+        # set parameters of training step
+
+        sgd.lr.set_value(lr)
+        sgd.decay.set_value(decay)
+        sgd.momentum.set_value(momentum)
+
+        reg.l1.set_value(l1)
+        reg.l2.set_value(l2)
+
+        # train model for one epoch_idx
+        H = model.fit(X, Y,
+                      batch_size=int(batch_size),
+                      nb_epoch=10,
+                      shuffle=True)
+
+
+        diverged = math.isnan(H.history['loss'][-1])
+        acc = 0.0
+
+        if not diverged:
+            _, acc = model.evaluate(Xv, Yv)
+
+        return diverged, acc
\ No newline at end of file