diff --git a/p_2/cbnn_tmux.py b/p_2/cbnn_tmux.py
deleted file mode 100644
index ad2f41883adc966948b4de2bba9e4aa619a271ee..0000000000000000000000000000000000000000
--- a/p_2/cbnn_tmux.py
+++ /dev/null
@@ -1,156 +0,0 @@
-cat_nr=100
-import pandas as pd
-pd.set_option('display.max_columns', 500)
-month_data_encoded=pd.read_excel(r"month_data_encoded.xlsx")
-mde=month_data_encoded
-mde=mde.drop(['dateRep', 'cases' , 'Rescd' , '7days_before_mean','7days_after_mean', 'week' ,'year' , 'index', 'month', 'countriesAndTerritories', 'va' , 'vaccin'], axis=1)
-mde = mde.reindex(sorted(mde.columns), axis=1)
-percent_list=[]
-number_list=[-10000]
-for percent,number in (enumerate(mde['rp_zeitraum'].describe([x/cat_nr for x in range (1,cat_nr)])[4:cat_nr+3].tolist())):
- percent_list.append(int(percent))
- number_list.append(number)
-
-percent_list.append(int(cat_nr-1))
-number_list.append(10000)
-mde['rp_zeitraum'] = pd.cut(x=mde['rp_zeitraum'], bins=number_list,labels=percent_list)
-
-import tensorflow as tf
-import tensorflow_probability as tfp
-import numpy as np
-import pandas as pd
-#import matplotlib.pyplot as plt
-#import seaborn as sns
-import time
-from sklearn.model_selection import train_test_split
-
-#%matplotlib inline
-#random seed as the birthday of my granp which is in the hospital fighting with cancer
-#be strong Valdomiro!
-#np.random.seed(10171927)
-#tf.random.set_seed(10171927)
-from sklearn.model_selection import train_test_split
-train, test = train_test_split(mde, test_size=0.10)
-Y_train = train["rp_zeitraum"]
-# Drop 'label' column
-X_train = train.drop(labels = ["rp_zeitraum"],axis = 1)
-X_train = X_train.values.reshape(-1,1,111,1)
-Y_train = tf.keras.utils.to_categorical(Y_train, num_classes = 100)
-X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.25, random_state=42)
-Y_t = test["rp_zeitraum"]
-# Drop 'label' column
-X_t = test.drop(labels = ["rp_zeitraum"],axis = 1)
-X_t = X_t.values.reshape(-1,1,111,1)
-Y_t = tf.keras.utils.to_categorical(Y_t, num_classes = 100)
-
-#ypred_test=X_t.tolist()
-
-with open('X_t.py', 'w') as w:
- w.write('X_t = %s' % X_t.tolist())
-with open('Y_t.py', 'w') as w:
- w.write('Y_t = %s' % Y_t.tolist())
-with open('X_train.py', 'w') as w:
- w.write('X_train = %s' % X_train.tolist())
-with open('Y_train.py', 'w') as w:
- w.write('Y_train = %s' % Y_train.tolist())
-with open('X_val.py', 'w') as w:
- w.write('X_val = %s' % X_val.tolist())
-with open('Y_val.py', 'w') as w:
- w.write('Y_val = %s' % Y_val.tolist())
-
-def build_bayesian_bcnn_model(input_shape):
-
- """
- Here we use tf.keras.Model to use our graph as a Neural Network:
- We select our input node as the net input, and the last node as our output (predict node).
- Note that our model won't be compiled, as we are usign TF2.0 and will optimize it with
- a custom @tf.function for loss and a @tf.function for train_step
- Our input parameter is just the input shape, a tuple, for the input layer
- """
-
- model_in = tf.keras.layers.Input(shape=input_shape)
- conv_1 = tfp.python.layers.Convolution2DFlipout(32, kernel_size=(1, 3), padding="same", strides=1)
- x = conv_1(model_in)
- x = tf.keras.layers.BatchNormalization()(x)
- x = tf.keras.layers.Activation('relu')(x)
- conv_2 = tfp.python.layers.Convolution2DFlipout(64, kernel_size=(1, 3), padding="same", strides=1)
- x = conv_2(x)
- x = tf.keras.layers.BatchNormalization()(x)
- x = tf.keras.layers.Activation('relu')(x)
- x = tf.keras.layers.Flatten()(x)
- dense_1 = tfp.python.layers.DenseFlipout(512, activation='relu')
- x = dense_1(x)
- dense_2 = tfp.python.layers.DenseFlipout(100, activation=None)
- model_out = dense_2(x) # logits
- model = tf.keras.Model(model_in, model_out)
- return model
-
-@tf.function
-def elbo_loss(labels, logits):
- loss_en = tf.nn.softmax_cross_entropy_with_logits(labels, logits)
- loss_kl = tf.keras.losses.KLD(labels, logits)
- loss = tf.reduce_mean(tf.add(loss_en, loss_kl))
- return [loss, tf.reduce_mean(loss_en), tf.reduce_mean(loss_kl)]
-
-@tf.function
-def train_step(images, labels):
- with tf.GradientTape() as tape:
- logits = bcnn(X_train)
- loss = elbo_loss(labels, logits)[0]
- gradients = tape.gradient(loss, bcnn.trainable_variables)
- optimizer.apply_gradients(zip(gradients, bcnn.trainable_variables))
- return loss
-
-def accuracy(preds, labels):
- #return np.mean(np.argmax(preds, axis=1) == np.argmax(labels, axis=1))
- predictednumbers=np.argmax(preds, axis=1)
- realnumbers=np.argmax(labels, axis=1)
- diff=np.subtract(predictednumbers , realnumbers)
- return np.sqrt(sum(np.power(diff, 2))/len(diff))
-
-bcnn = build_bayesian_bcnn_model(X_train.shape[1:])
-optimizer = tf.keras.optimizers.Adam(lr=0.001)
-
-times = []
-accs = []
-val_accs = []
-losses = []
-val_losses = []
-val_losses_en = []
-val_losses_kl = []
-for i in range(1000):
- #tic = time.time()
- loss = train_step(X_train, Y_train).numpy()
- preds = bcnn(X_train)
- acc = accuracy(preds, Y_train)
- accs.append(acc)
- losses.append(loss)
-
- val_preds = bcnn(X_val)
- val_loss = elbo_loss(Y_val, val_preds)[0].numpy()
- val_loss_en=elbo_loss(Y_val, val_preds)[1].numpy()
- val_loss_kl=elbo_loss(Y_val, val_preds)[2].numpy()
- val_acc = accuracy(Y_val, val_preds)
-
- val_accs.append(val_acc)
- val_losses.append(val_loss)
- val_losses_en.append(val_loss_en)
- val_losses_kl.append(val_loss_kl)
- #tac = time.time()
- #train_time = tac-tic
- #times.append(train_time)
-
- print("Epoch: {}: loss = {:7.3f} , accuracy = {:7.3f}, val_loss = {:7.3f}, val_acc={:7.3f} ".format(i, loss, acc, val_loss, val_acc))
-bcnn.save("convmodel")
-with open('accs.py', 'w') as w:
- w.write('accs = %s' % accs)
-with open('losses.py', 'w') as w:
- w.write('losses = %s' % losses)
-with open('val_accs.py', 'w') as w:
- w.write('val_accs = %s' % val_accs)
-with open('val_losses.py', 'w') as w:
- w.write('val_losses = %s' % val_losses)
-with open('val_losses_en.py', 'w') as w:
- w.write('val_losses_en = %s' % val_losses_en)
-with open('val_losses_kl.py', 'w') as w:
- w.write('val_losses_kl = %s' % val_losses_kl)
\ No newline at end of file