--- a
+++ b/code/cnn_model.py
@@ -0,0 +1,75 @@
+from models import get_model_cnn
+import numpy as np
+from utils import gen, chunker, WINDOW_SIZE, rescale_array
+from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
+from sklearn.metrics import f1_score, accuracy_score, classification_report
+from glob import glob
+import os
+from sklearn.model_selection import train_test_split
+from tqdm import tqdm
+
+
+base_path = "/media/ml/data_ml/EEG/deepsleepnet/data_npy"
+
+files = sorted(glob(os.path.join(base_path, "*.npz")))
+
+ids = sorted(list(set([x.split("/")[-1][:5] for x in files])))
+#split by test subject
+train_ids, test_ids = train_test_split(ids, test_size=0.15, random_state=1338)
+
+train_val, test = [x for x in files if x.split("/")[-1][:5] in train_ids],\
+                  [x for x in files if x.split("/")[-1][:5] in test_ids]
+
+train, val = train_test_split(train_val, test_size=0.1, random_state=1337)
+
+train_dict = {k: np.load(k) for k in train}
+test_dict = {k: np.load(k) for k in test}
+val_dict = {k: np.load(k) for k in val}
+
+model = get_model_cnn()
+
+file_path = "cnn_model.h5"
+# model.load_weights(file_path)
+
+checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
+early = EarlyStopping(monitor="val_acc", mode="max", patience=20, verbose=1)
+redonplat = ReduceLROnPlateau(monitor="val_acc", mode="max", patience=5, verbose=2)
+callbacks_list = [checkpoint, early, redonplat]  # early
+
+model.fit_generator(gen(train_dict, aug=False), validation_data=gen(val_dict), epochs=100, verbose=2,
+                    steps_per_epoch=1000, validation_steps=300, callbacks=callbacks_list)
+model.load_weights(file_path)
+
+
+preds = []
+gt = []
+
+for record in tqdm(test_dict):
+    all_rows = test_dict[record]['x']
+    for batch_hyp in chunker(range(all_rows.shape[0])):
+
+
+        X = all_rows[min(batch_hyp):max(batch_hyp)+1, ...]
+        Y = test_dict[record]['y'][min(batch_hyp):max(batch_hyp)+1]
+
+        X = np.expand_dims(X, 0)
+
+        X = rescale_array(X)
+
+        Y_pred = model.predict(X)
+        Y_pred = Y_pred.argmax(axis=-1).ravel().tolist()
+
+        gt += Y.ravel().tolist()
+        preds += Y_pred
+
+
+
+f1 = f1_score(gt, preds, average="macro")
+
+print("Seq Test f1 score : %s "% f1)
+
+acc = accuracy_score(gt, preds)
+
+print("Seq Test accuracy score : %s "% acc)
+
+print(classification_report(gt, preds))
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