Easy Keras Models and Plotting

nn.py

import numpy as np
import tensorflow
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.utils import to_categorical
from keras.callbacks import EarlyStopping
from keras.optimizers import SGD, Adam
from keras.callbacks import History
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

#Define a function which creates a keras model with hidden layers, activation functions, and dropout rates
def simple_NN(input_shape, nodes_per=[60], hidden=0, out=2, act_out='softmax', act_hid='relu', drop=True, d_rate=0.1):
  """Generate a keras neural network with arbitrary number of hidden layers, activation functions, dropout rates, etc"""
  model = Sequential()
  #adding first hidden layer with 60 nodes (first value in nodes_per list)
  model.add(Dense(nodes_per[0],activation=act_hid,input_shape=input_shape))
  if drop:
      model.add(Dropout(d_rate))
  try:
    if hidden != 0:
      for i,j in zip(range(hidden), nodes_per[1:]):
          model.add(Dense(j,activation=act_hid))
          if drop:
              model.add(Dropout(d_rate))
    model.add(Dense(out,activation=act_out))
    return(model)
  except:
    print('Error in generating hidden layers')

#Define a function to plot historical data on key statistics of a keras model
def plt_perf(name, p_loss=False, p_acc=False, val=False, size=(15,15), save=False):
  """Plot model statistics for keras models"""
  if p_loss or p_acc:
    if p_loss:
      plt.figure(figsize = size)
      plt.title('Loss')
      plt.plot(name.history['loss'], 'b', label='loss')
      if val:
        plt.plot(name.history['val_loss'], 'r', label='val_loss')
      plt.xlabel('Epochs')
      plt.ylabel('Value')
      plt.legend()
      plt.show()
      if save:
        plt.savefig('loss.png')
    if p_acc:
      plt.figure(figsize = size)
      plt.title('Accuracy')
      plt.plot(name.history['acc'], 'b', label='acc')
      if val:
        plt.plot(name.history['val_acc'], 'r', label='val_acc')
      plt.xlabel('Epochs')
      plt.ylabel('Value')
      plt.legend()
      plt.show()
      if save:
        plt.savefig('acc.png')
  else:
    print('No plotting since all parameters set to false.')

See: https://www.kaggle.com/limitpointinf0/leaf-classifier-knn-nn
for example of implementation