Separated classification and regression functions

Author: tirthajyoti

Notebooks/utils/DL_utils.py

@@ -1,109 +1,175 @@
-# Utility functions for deep learning with Keras
-# Dr. Tirthajyoti Sarkar, Fremont, CA 94536
-
-import tensorflow as tf
-import numpy as np
-import matplotlib.pyplot as plt
-
-class myCallback(tf.keras.callbacks.Callback):
-  """
-  User can pass on the desired accuracy threshold while creating an instance of the class
-  """
-  def __init__(self,acc_threshold=0.9,print_msg=True):
-    self.acc_threshold=acc_threshold
-    self.print_msg = print_msg
-    
-  def on_epoch_end(self, epoch, logs={}):
-    if(logs.get('acc')>self.acc_threshold):
-      if self.print_msg:
-        print("\nReached 90% accuracy so cancelling the training!")
-      self.model.stop_training = True
-    else:
-      if self.print_msg:
-        print("\nAccuracy not high enough. Starting another epoch...\n")
-
-
-def build_model(num_layers=1, architecture=[32],act_func='relu', 
-                input_shape=(28,28), output_class=10):
-  """
-  Builds a densely connected neural network model from user input
-  num_layers: Number of hidden layers
-  architecture: Architecture of the hidden layers (densely connected)
-  act_func: Activation function. Could be 'relu', 'sigmoid', or 'tanh'.
-  input_shape: Dimension of the input vector
-  output_class: Number of classes in the output vector
-  """
-  layers=[tf.keras.layers.Flatten(input_shape=input_shape)]
-  if act_func=='relu':
-    activation=tf.nn.relu
-  elif act_func=='sigmoid':
-    activation=tf.nn.sigmoid
-  elif act_func=='tanh':
-    activation=tf.nn.tanh
-    
-  for i in range(num_layers):
-    layers.append(tf.keras.layers.Dense(architecture[i], activation=tf.nn.relu))
-  layers.append(tf.keras.layers.Dense(output_class, activation=tf.nn.softmax))
-  
-  model = tf.keras.models.Sequential(layers)
-  return model
-
-
-def compile_train_model(model,x_train, y_train, callbacks=None,
-                        learning_rate=0.001,batch_size=1,epochs=10,verbose=0):
-  """
-  Compiles and trains a given Keras model with the given data. 
-  Assumes Adam optimizer for this implementation.
-  
-  # Arguments
-  learning_rate: Learning rate for the optimizer Adam
-  batch_size: Batch size for the mini-batch optimization
-  epochs: Number of epochs to train
-  verbose: Verbosity of the training process
-  
-  # Returns
-  A copy of the model
-  """
-  
-  model_copy = model
-  model_copy.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
-              loss='sparse_categorical_crossentropy',
-              metrics=['accuracy'])
-  
-  model_copy.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,
-                 callbacks=[callbacks],verbose=verbose)
-  return model_copy
-
-
-
-def plot_loss_acc(model,target_acc=0.9, title=None):
-  """
-  Takes a deep learning model and plots the loss ans accuracy over epochs
-  Users can supply a title if needed
-  target_acc: The desired/ target acc. This parameter is needed for this function to show a horizontal bar.
-  """
-  e=np.array(model.history.epoch)+1 # Add one to the list of epochs which is zero-indexed
-  l=np.array(model.history.history['loss'])
-  a=np.array(model.history.history['acc'])
-  
-  fig, ax1 = plt.subplots()
-
-  color = 'tab:red'
-  ax1.set_xlabel('Epochs',fontsize=15)
-  ax1.set_ylabel('Loss', color=color,fontsize=15)
-  ax1.plot(e, l, color=color,lw=2)
-  ax1.tick_params(axis='y', labelcolor=color)
-  ax1.grid(True)
-
-  ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
-
-  color = 'tab:blue'
-  ax2.set_ylabel('Accuracy', color=color,fontsize=15)  # we already handled the x-label with ax1
-  ax2.plot(e, a, color=color,lw=2)
-  ax2.tick_params(axis='y', labelcolor=color)
-
-  fig.tight_layout()  # otherwise the right y-label is slightly clipped
-  if title!=None:
-    plt.title(title)
-  plt.hlines(y=target_acc,xmin=1,xmax=e.max(),colors='k', linestyles='dashed',lw=3)
+# Utility functions for deep learning with Keras
+# Dr. Tirthajyoti Sarkar, Fremont, CA 94536
+
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+
+class myCallback(tf.keras.callbacks.Callback):
+  """
+  User can pass on the desired accuracy threshold while creating an instance of the class
+  """
+  def __init__(self,acc_threshold=0.9,print_msg=True):
+    self.acc_threshold=acc_threshold
+    self.print_msg = print_msg
+    
+  def on_epoch_end(self, epoch, logs={}):
+    if(logs.get('acc')>self.acc_threshold):
+      if self.print_msg:
+        print("\nReached 90% accuracy so cancelling the training!")
+      self.model.stop_training = True
+    else:
+      if self.print_msg:
+        print("\nAccuracy not high enough. Starting another epoch...\n")
+
+
+def build_classification_model(num_layers=1, architecture=[32],act_func='relu', 
+                input_shape=(28,28), output_class=10):
+  """
+  Builds a densely connected neural network model from user input
+  
+  Arguments
+          num_layers: Number of hidden layers
+          architecture: Architecture of the hidden layers (densely connected)
+          act_func: Activation function. Could be 'relu', 'sigmoid', or 'tanh'.
+          input_shape: Dimension of the input vector
+          output_class: Number of classes in the output vector
+  Returns
+          A neural net (Keras) model for classification
+  """
+  layers=[tf.keras.layers.Flatten(input_shape=input_shape)]
+  if act_func=='relu':
+    activation=tf.nn.relu
+  elif act_func=='sigmoid':
+    activation=tf.nn.sigmoid
+  elif act_func=='tanh':
+    activation=tf.nn.tanh
+    
+  for i in range(num_layers):
+    layers.append(tf.keras.layers.Dense(architecture[i], activation=tf.nn.relu))
+  layers.append(tf.keras.layers.Dense(output_class, activation=tf.nn.softmax))
+  
+  model = tf.keras.models.Sequential(layers)
+  return model
+
+def build_regression_model(input_neurons=10,input_dim=1,num_layers=1, architecture=[32],act_func='relu'):
+  """
+  Builds a densely connected neural network model from user input
+  
+  Arguments
+          num_layers: Number of hidden layers
+          architecture: Architecture of the hidden layers (densely connected)
+          act_func: Activation function. Could be 'relu', 'sigmoid', or 'tanh'.
+          input_shape: Dimension of the input vector
+  Returns
+          A neural net (Keras) model for regression
+  """
+  if act_func=='relu':
+    activation=tf.nn.relu
+  elif act_func=='sigmoid':
+    activation=tf.nn.sigmoid
+  elif act_func=='tanh':
+    activation=tf.nn.tanh
+  
+  layers=[tf.keras.layers.Dense(input_neurons,input_dim=input_dim,activation=activation)]
+     
+  for i in range(num_layers):
+    layers.append(tf.keras.layers.Dense(architecture[i], activation=activation))
+  layers.append(tf.keras.layers.Dense(1))
+  
+  model = tf.keras.models.Sequential(layers)
+  return model
+
+
+def compile_train_classification_model(model,x_train, y_train, callbacks=None,
+                        learning_rate=0.001,batch_size=1,epochs=10,verbose=0):
+  """
+  Compiles and trains a given Keras model with the given data. 
+  Assumes Adam optimizer for this implementation.
+  Assumes categorical cross-entropy loss.
+  
+  Arguments
+          learning_rate: Learning rate for the optimizer Adam
+          batch_size: Batch size for the mini-batch optimization
+          epochs: Number of epochs to train
+          verbose: Verbosity of the training process
+  
+  Returns
+  A copy of the model
+  """
+  
+  model_copy = model
+  model_copy.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+  
+  if callbacks!=None:
+    model_copy.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,
+                 callbacks=[callbacks],verbose=verbose)
+  else:
+    model_copy.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,
+                 verbose=verbose)
+  return model_copy
+
+def compile_train_regression_model(model,x_train, y_train, callbacks=None,
+                        learning_rate=0.001,batch_size=1,epochs=10,verbose=0):
+  """
+  Compiles and trains a given Keras model with the given data for regression. 
+  Assumes Adam optimizer for this implementation.
+  Assumes mean-squared-error loss
+  
+  Arguments
+          learning_rate: Learning rate for the optimizer Adam
+          batch_size: Batch size for the mini-batch operation
+          epochs: Number of epochs to train
+          verbose: Verbosity of the training process
+  
+  Returns
+  A copy of the model
+  """
+  
+  model_copy = model
+  model_copy.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
+              loss='mean_squared_error',
+              metrics=['accuracy'])
+  if callbacks!=None:
+    model_copy.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,
+                 callbacks=[callbacks],verbose=verbose)
+  else:
+    model_copy.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,
+                 verbose=verbose)
+  return model_copy
+
+
+
+def plot_loss_acc(model,target_acc=0.9, title=None):
+  """
+  Takes a deep learning model and plots the loss ans accuracy over epochs
+  Users can supply a title if needed
+  target_acc: The desired/ target acc. This parameter is needed for this function to show a horizontal bar.
+  """
+  e=np.array(model.history.epoch)+1 # Add one to the list of epochs which is zero-indexed
+  l=np.array(model.history.history['loss'])
+  a=np.array(model.history.history['acc'])
+  
+  fig, ax1 = plt.subplots()
+
+  color = 'tab:red'
+  ax1.set_xlabel('Epochs',fontsize=15)
+  ax1.set_ylabel('Loss', color=color,fontsize=15)
+  ax1.plot(e, l, color=color,lw=2)
+  ax1.tick_params(axis='y', labelcolor=color)
+  ax1.grid(True)
+
+  ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
+
+  color = 'tab:blue'
+  ax2.set_ylabel('Accuracy', color=color,fontsize=15)  # we already handled the x-label with ax1
+  ax2.plot(e, a, color=color,lw=2)
+  ax2.tick_params(axis='y', labelcolor=color)
+
+  fig.tight_layout()  # otherwise the right y-label is slightly clipped
+  if title!=None:
+    plt.title(title)
+  plt.hlines(y=target_acc,xmin=1,xmax=e.max(),colors='k', linestyles='dashed',lw=3)
   plt.show()