FNN for my project

FNN.py

import numpy as np

class TwoLayerNet(object):
	"""
	A two-layer fully-connected neural network. The net has an input dimension of
	N, a hidden layer dimension of H, and performs classification over C classes.
	We train the network with a softmax loss function and L2 regularization on the
	weight matrices. The network uses a ReLU nonlinearity after the first fully
	connected layer.

	In other words, the network has the following architecture:

	input - fully connected layer - ReLU - fully connected layer - softmax

	The outputs of the second fully-connected layer are the scores for each class.
	"""

	def __init__(self, input_size, hidden_size, output_size, std):
		"""
		Initialize the model. Weights are initialized following Xavier intialization and
		biases are initialized to zero. Weights and biases are stored in the
		variable self.params, which is a dictionary with the following keys:

		W1: First layer weights; has shape (D, H)
		b1: First layer biases; has shape (H,)
		W2: Second layer weights; has shape (H, C)
		b2: Second layer biases; has shape (C,)

		Inputs:
		- input_size: The dimension D of the input data.
		- hidden_size: The number of neurons H in the hidden layer.
		- output_size: The number of classes C.
		"""
		self.params = {}
		self.params['W1'] = ((2 / input_size) ** 0.5) * np.random.randn(input_size, hidden_size)
		self.params['b1'] = np.zeros(hidden_size)
		self.params['W2'] = ((2 / hidden_size) ** 0.5) * np.random.randn(hidden_size, output_size)
		self.params['b2'] = np.zeros(output_size)

	def loss(self, X, y=None, reg=0.0):
		"""
		Compute the loss and gradients for a two layer fully connected neural
		network.

		Inputs:
		- X: Input data of shape (N, D). Each X[i] is a training sample.
		- y: Vector of training labels. y[i] is the label for X[i], and each y[i] is
		  an integer in the range 0 <= y[i] < C. This parameter is optional; if it
		  is not passed then we only return scores, and if it is passed then we
		  instead return the loss and gradients.
		- reg: Regularization strength.

		Returns:
		- loss: Loss (data loss and regularization loss) for this batch of training
		  samples.
		- grads: Dictionary mapping parameter names to gradients of those parameters
		  with respect to the loss function; has the same keys as self.params.
		"""
		# Unpack variables from the params dictionary
		W1, b1 = self.params['W1'], self.params['b1']
		W2, b2 = self.params['W2'], self.params['b2']
		N, D = X.shape

		# Compute the forward pass
		l1 = X.dot(W1) + b1
		l1[l1 < 0] = 0
		l2 = l1.dot(W2) + b2
		exp_scores = np.exp(l2)
		probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
		scores = l2

		# Compute the loss

		W1_r = 0.5 * reg * np.sum(W1 * W1)
		W2_r = 0.5 * reg * np.sum(W2 * W2)

		loss = -np.sum(np.log(probs[range(y.shape[0]), y])) / N + W1_r + W2_r


		# Backward pass: compute gradients
		grads = {}
		
		probs[range(X.shape[0]),y] -= 1
		dW2 = np.dot(l1.T, probs)
		dW2 /= X.shape[0]
		dW2 += reg * W2
		grads['W2'] = dW2
		grads['b2'] = np.sum(probs, axis=0, keepdims=True) / X.shape[0]
		
		delta = probs.dot(W2.T)
		delta = delta * (l1 > 0)
		grads['W1'] = np.dot(X.T, delta)/ X.shape[0] + reg * W1
		grads['b1'] = np.sum(delta, axis=0, keepdims=True) / X.shape[0]

		return loss, grads

	def train(self, X, y, X_val, y_val,
            learning_rate=1e-3, learning_rate_decay=0.95,
            reg=5e-6, num_iters=100,
            batch_size=24, verbose=False):
		"""
		Train this neural network using stochastic gradient descent.

		Inputs:
		- X: A numpy array of shape (N, D) giving training data.
		- y: A numpy array f shape (N,) giving training labels; y[i] = c means that
		  X[i] has label c, where 0 <= c < C.
		- X_val: A numpy array of shape (N_val, D) giving validation data.
		- y_val: A numpy array of shape (N_val,) giving validation labels.
		- learning_rate: Scalar giving learning rate for optimization.
		- learning_rate_decay: Scalar giving factor used to decay the learning rate
		  after each epoch.
		- reg: Scalar giving regularization strength.
		- num_iters: Number of steps to take when optimizing.
		- batch_size: Number of training examples to use per step.
		- verbose: boolean; if true print progress during optimization.
		"""
		num_train = X.shape[0]
		iterations_per_epoch = max(num_train / batch_size, 1)

		# Use SGD to optimize the parameters in self.model
		loss_history = []
		train_acc_history = []
		val_acc_history = []

		for it in range(num_iters):
			indexes = np.random.choice(X.shape[0], batch_size, replace=True)
			X_batch = X[indexes]
			y_batch = y[indexes]
			# Compute loss and gradients using the current minibatch
			loss, grads = self.loss(X_batch, y=y_batch, reg=reg)
			loss_history.append(loss)


			self.params['W1'] -= learning_rate * grads['W1']
			self.params['b1'] -= learning_rate * grads['b1'][0]
			self.params['W2'] -= learning_rate * grads['W2']
			self.params['b2'] -= learning_rate * grads['b2'][0]

			if verbose and it % 100 == 0:
				print('iteration %d / %d: loss %f' % (it, num_iters, loss))

			# Every epoch, check train and val accuracy and decay learning rate.
			if it % iterations_per_epoch == 0:
				# Check accuracy
				train_acc = (self.predict(X_batch) == y_batch).mean()
				val_acc = (self.predict(X_val) == y_val).mean()
				train_acc_history.append(train_acc)
				val_acc_history.append(val_acc)

				# Decay learning rate
				learning_rate *= learning_rate_decay

		return {
		  'loss_history': loss_history,
		  'train_acc_history': train_acc_history,
		  'val_acc_history': val_acc_history,
		}

	def predict(self, X):
		"""
		Use the trained weights of this two-layer network to predict labels for
		data points. For each data point we predict scores for each of the C
		classes, and assign each data point to the class with the highest score.

		Inputs:
		- X: A numpy array of shape (N, D) giving N D-dimensional data points to
		  classify.

		Returns:
		- y_pred: A numpy array of shape (N,) giving predicted labels for each of
		  the elements of X. For all i, y_pred[i] = c means that X[i] is predicted
		  to have class c, where 0 <= c < C.
		"""
		l1 = X.dot(self.params['W1']) + self.params['b1']
		l1[l1 < 0] = 0
		l2 = l1.dot(self.params['W2']) + self.params['b2']
		exp_scores = np.exp(l2)
		probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
		y_pred = np.argmax(probs, axis=1)

		return y_pred

	def predict_single(self, X):
		"""
		Use the trained weights of this two-layer network to predict labels for
		data point. We predict scores for each of the C
		classes, and assign еру data point to the class with the highest score.

		Inputs:
		- X: A numpy array of shape (N, D) giving N D-dimensional data points to
		  classify.

		Returns:
		- y_pred: A numpy array of shape (1,) giving predicted labels for X.
		"""
		l1 = X.dot(self.params['W1']) + self.params['b1']
		l1[l1 < 0] = 0
		l2 = l1.dot(self.params['W2']) + self.params['b2']
		exp_scores = np.exp(l2)
		y_pred = np.argmax(exp_scores)
		
		return y_pred