--- a
+++ b/code/baseline_mitbih.py
@@ -0,0 +1,72 @@
+import pandas as pd
+import numpy as np
+
+from keras import optimizers, losses, activations, models
+from keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler, ReduceLROnPlateau
+from keras.layers import Dense, Input, Dropout, Convolution1D, MaxPool1D, GlobalMaxPool1D, GlobalAveragePooling1D, \
+    concatenate
+from sklearn.metrics import f1_score, accuracy_score
+
+
+df_train = pd.read_csv("../input/mitbih_train.csv", header=None)
+df_train = df_train.sample(frac=1)
+df_test = pd.read_csv("../input/mitbih_test.csv", header=None)
+
+Y = np.array(df_train[187].values).astype(np.int8)
+X = np.array(df_train[list(range(187))].values)[..., np.newaxis]
+
+Y_test = np.array(df_test[187].values).astype(np.int8)
+X_test = np.array(df_test[list(range(187))].values)[..., np.newaxis]
+
+
+def get_model():
+    nclass = 5
+    inp = Input(shape=(187, 1))
+    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
+    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
+    img_1 = MaxPool1D(pool_size=2)(img_1)
+    img_1 = Dropout(rate=0.1)(img_1)
+    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
+    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
+    img_1 = MaxPool1D(pool_size=2)(img_1)
+    img_1 = Dropout(rate=0.1)(img_1)
+    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
+    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
+    img_1 = MaxPool1D(pool_size=2)(img_1)
+    img_1 = Dropout(rate=0.1)(img_1)
+    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
+    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
+    img_1 = GlobalMaxPool1D()(img_1)
+    img_1 = Dropout(rate=0.2)(img_1)
+
+    dense_1 = Dense(64, activation=activations.relu, name="dense_1")(img_1)
+    dense_1 = Dense(64, activation=activations.relu, name="dense_2")(dense_1)
+    dense_1 = Dense(nclass, activation=activations.softmax, name="dense_3_mitbih")(dense_1)
+
+    model = models.Model(inputs=inp, outputs=dense_1)
+    opt = optimizers.Adam(0.001)
+
+    model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
+    model.summary()
+    return model
+
+model = get_model()
+file_path = "baseline_cnn_mitbih.h5"
+checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
+early = EarlyStopping(monitor="val_acc", mode="max", patience=5, verbose=1)
+redonplat = ReduceLROnPlateau(monitor="val_acc", mode="max", patience=3, verbose=2)
+callbacks_list = [checkpoint, early, redonplat]  # early
+
+model.fit(X, Y, epochs=1000, verbose=2, callbacks=callbacks_list, validation_split=0.1)
+model.load_weights(file_path)
+
+pred_test = model.predict(X_test)
+pred_test = np.argmax(pred_test, axis=-1)
+
+f1 = f1_score(Y_test, pred_test, average="macro")
+
+print("Test f1 score : %s "% f1)
+
+acc = accuracy_score(Y_test, pred_test)
+
+print("Test accuracy score : %s "% acc)
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