Merge branch 'codestyle' into 'master'

Fix Python 3 compatibility and codestyle

See merge request JGlombitza/HAP_Tutorial_2018!1

AirShower_WGAN/ShowerGAN.py

 import numpy as np
-from tensorflow import keras
-from keras.layers import *
-from keras.models import Sequential, Model
+from keras.layers import BatchNormalization, Conv2D, Dense, GlobalMaxPooling2D, Reshape, UpSampling2D
+from keras.models import Sequential
 from keras.optimizers import Adam
 from keras.layers.advanced_activations import LeakyReLU
 from functools import partial
 from keras.utils import plot_model
 import keras.backend.tensorflow_backend as KTF
 import utils
-import tensorflow as tf
 
 
 KTF.set_session(utils.get_session())  # Allows 2 jobs per GPU, Please do not change this during the tutorial
@@ -100,7 +98,7 @@ critic_loss = []
 # trainings loop
 iterations_per_epoch = N//((NCR+1)*BATCH_SIZE)
 for epoch in range(EPOCHS):
-    print "epoch: ", epoch
+    print("epoch: ", epoch)
     # plot berfore each epoch generated signal patterns
     generated_map = generator.predict_on_batch(np.random.randn(BATCH_SIZE, latent_size))
     utils.plot_multiple_signalmaps(generated_map[:, :, :, 0], log_dir=log_dir, title='Generated Footprints Epoch: ', epoch=str(epoch))
@@ -111,11 +109,11 @@ for epoch in range(EPOCHS):
             noise_batch = np.random.randn(BATCH_SIZE, latent_size)  # generate noise batch for generator
             shower_batch = shower_maps[BATCH_SIZE*(j+iteration):BATCH_SIZE*(j++iteration+1)]  # take batch of shower maps
             critic_loss.append(critic_training.train_on_batch([noise_batch, shower_batch], [negative_y, positive_y, dummy]))  # train the critic
-            print "critic loss:", critic_loss[-1]
+            print("critic loss:", critic_loss[-1])
 
         noise_batch = np.random.randn(BATCH_SIZE, latent_size)  # generate noise batch for generator
         generator_loss.append(generator_training.train_on_batch([noise_batch], [positive_y]))  # train the generator
-        print "generator loss:", generator_loss[-1]
+        print("generator loss:", generator_loss[-1])
 
 # plot critic and generator loss
 utils.plot_loss(critic_loss, name="critic", log_dir=log_dir)

AirShower_WGAN/utils.py

 import numpy as np
 import tensorflow as tf
-from tensorflow import keras
-from keras.layers import *
-from keras.models import Sequential, Model
+from keras.layers import Input
+from keras.models import Model
 from keras.layers.merge import _Merge
+from keras import backend as K
 import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
@@ -111,7 +111,7 @@ def plot_signal_map(footprint, axis, label=None):
     cbar = plt.colorbar(circles, ax=axis)
     cbar.set_label('signal [a.u.]')
     axis.grid(True)
-    if label!=None:
+    if label is not None:
         axis.text(0.95, 0.1, "Energy: %.1f EeV" % label, verticalalignment='top', horizontalalignment='right', transform=axis.transAxes, backgroundcolor='w')
     axis.set_aspect('equal')
     axis.set_xlim(-5, 5)

MNIST_GAN/MNIST_GAN.py

@@ -72,14 +72,14 @@ y[ntrain:, 0] = 1             # class 0 for generated images
 discriminator.fit(X, y, epochs=1, batch_size=128)
 
 # - Create a dataset of 5000 real test images and 5000 fake images.
-no = np.random.choice(10000, size=ntrain/2, replace='False')
+no = np.random.choice(10000, size=ntrain//2, replace='False')
 real_test = X_test[no, :, :, :]   # sample real images from test set
-noise_gen = np.random.uniform(0,1,size=[ntrain/2, latent_dim])
+noise_gen = np.random.uniform(0, 1, size=[ntrain//2, latent_dim])
 generated_images = generator.predict(noise_gen)    # generate fake images with untrained generator
 Xt = np.concatenate((real_test, generated_images))
 yt = np.zeros([ntrain, 2])   # class vector: one-hot encoding
-yt[:ntrain/2, 1] = 1         # class 1 for real images
-yt[ntrain/2:, 0] = 1         # class 0 for generated images
+yt[:ntrain//2, 1] = 1         # class 1 for real images
+yt[ntrain//2:, 0] = 1         # class 0 for generated images
 
 # - Evaluate the test accuracy of your network.
 pretrain_loss, pretrain_acc = discriminator.evaluate(Xt, yt, verbose=0, batch_size=128)
@@ -89,9 +89,9 @@ print('Test accuracy: %04f' % pretrain_acc)
 losses = {"d": [], "g": []}
 discriminator_acc = []
 
+
 # main training loop
 def train_for_n(epochs=1, batch_size=32):
-    
     for epoch in range(epochs):
 
         # Plot some fake images
@@ -99,7 +99,7 @@ def train_for_n(epochs=1, batch_size=32):
         generated_images = generator.predict(noise)
         plot_images(generated_images, fname=log_dir + '/generated_images_' + str(epoch))
 
-        iterations_per_epoch = 60000/batch_size    # number of training steps per epoch
+        iterations_per_epoch = 60000//batch_size    # number of training steps per epoch
         perm = np.random.choice(60000, size=60000, replace='False')
 
         for i in range(iterations_per_epoch):
@@ -127,6 +127,7 @@ def train_for_n(epochs=1, batch_size=32):
             g_loss = GAN.train_on_batch(noise_tr, y2)
             losses["g"].append(g_loss)
 
+
 train_for_n(epochs=10, batch_size=128)
 
 # - Plot the loss of discriminator and generator as function of iterations

MNIST_GAN/gan.py

 import numpy as np
 import matplotlib.pyplot as plt
-from keras.layers import *
+from keras.layers import Activation, BatchNormalization, Conv2D, Dense, Dropout, Flatten, Reshape, UpSampling2D
 from keras.layers.advanced_activations import LeakyReLU
 from keras.regularizers import l1_l2
 from keras.models import Sequential

MNIST_GAN/mnist.py

@@ -10,7 +10,7 @@ import numpy as np
 import gzip
 import os
 import json
-import matplotlib.pyplot as plt
+
 
 def get_datapath(fname=''):
     """Get data path.
@@ -36,15 +36,6 @@ class Dataset():
         """
         self.__dict__.update(kwds)
 
-    def plot_examples(self, num_examples=5, fname=None):
-        """Plot examples from the dataset
-
-        Args:
-            num_examples (int, optional): number of examples to
-            fname (str, optional): filename for saving the plot
-        """
-        plot_examples(self, num_examples, fname)
-
 
 def load_data():
     """Load the MNIST dataset.