--- a
+++ b/old_keras_impl/run.py
@@ -0,0 +1,81 @@
+from keras.layers import Convolution1D
+from keras.models import Sequential
+from keras.layers import Activation, Dense, Flatten, Dropout
+from data import process_data
+from keras import backend as K
+import numpy  as np
+import os
+
+if not os.listdir('datasets/processed'):
+    process_data()
+
+arrhy_data = np.loadtxt(open('datasets/processed/arrhythmia.csv', 'r'), skiprows=1)
+malignant_data = np.loadtxt(open('datasets/processed/malignant-ventricular-ectopy.csv', 'r'), skiprows=1)
+arrhy_data = arrhy_data[:len(malignant_data)]
+arrhy_len = len(arrhy_data)/500
+
+i = 0
+X_train = []
+inter_X_train = []
+inter_y_train = []
+y_train = []
+nb_filters = 32
+nb_epoch = 10
+batch_size = 8
+counter = 0
+
+for _ in range(arrhy_len):
+    counter += 1
+    if not (counter % batch_size):
+        X_train.append(inter_X_train)
+        y_train.append(inter_y_train)
+        inter_X_train = []
+        inter_y_train = []
+
+    inter_X_train.append(np.asarray(arrhy_data[i:i+500]))
+    inter_y_train.append(0)
+    inter_X_train.append(np.asarray(malignant_data[i:i+500]))
+    inter_y_train.append(1)
+    i += 500
+
+validation_size = int(0.1  * len(X_train))
+
+# remove the bugged batch
+X_train.pop(0)
+y_train.pop(0)
+
+# split training and testing sets
+X_train, X_test = np.split(X_train, [len(X_train)-validation_size])
+y_train, y_test = np.split(y_train, [len(y_train)-validation_size])
+
+# checking batch lengths
+for batch in X_train:
+    if len(batch) != 16:
+        print("uneven batch with len: {}".format(len(batch)))
+    for example in batch:
+        if len(example) != 500:
+            print("uneven example with len: {}".format(len(example)))
+
+
+# shape = (X_train.shape[0], 16, 500)
+shape = X_train.shape[1:]
+
+# in numpy if arrays are not the same shape they will not appear in the .shape method
+print("shape: {}".format(shape))
+
+model = Sequential()
+model.add(Convolution1D(nb_filters, 3, input_shape=shape, activation='relu'))
+model.add(Dropout(0.25))
+model.add(Convolution1D(nb_filters, 3, activation='relu'))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(256, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(1))
+model.add(Activation('softmax'))
+
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+print("ok")
+model.fit(X_train, y_train, batch_size=batch_size,
+          nb_epoch=nb_epoch, validation_data=(X_test, y_test))
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