code.py

import numpy as np
import nnfs
import emnist
import os
import cv2
import pickle
import copy
nnfs.init()
# Dense layer
class Layer_Dense:
	# Layer initialization
	def __init__(self, n_inputs, n_neurons,
		weight_regularizer_l1=0, weight_regularizer_l2=0,
		bias_regularizer_l1=0, bias_regularizer_l2=0):

		# Initialize weights and biases
		self.weights = 0.01 * np.random.randn(n_inputs, n_neurons)
		self.biases = np.zeros((1, n_neurons))
		# Set regularization strength
		self.weight_regularizer_l1 = weight_regularizer_l1
		self.weight_regularizer_l2 = weight_regularizer_l2
		self.bias_regularizer_l1 = bias_regularizer_l1
		self.bias_regularizer_l2 = bias_regularizer_l2
		# Forward pass
	def forward(self, inputs, training):
		# Remember input values
		self.inputs = inputs
		inputs = np.reshape(inputs,-1,28**2)
		# Calculate output values from inputs, weights and biases
		self.output = np.dot(inputs, self.weights) + self.biases
		# Backward pass
	def backward(self, dvalues):
		# Gradients on parameters
		self.dweights = np.dot(self.inputs.T, dvalues)
		self.dbiases = np.sum(dvalues, axis=0, keepdims=True)
		# Gradients on regularization
		# L1 on weights
		if self.weight_regularizer_l1 > 0:
			dL1 = np.ones_like(self.weights)
			dL1[self.weights < 0] = -1
			self.dweights += self.weight_regularizer_l1 * dL1
		# L2 on weights
		if self.weight_regularizer_l2 > 0:
			self.dweights += 2 * self.weight_regularizer_l2 * \
			self.weights
		# L1 on biases
		if self.bias_regularizer_l1 > 0:
			dL1 = np.ones_like(self.biases)
			dL1[self.biases < 0] = -1
			self.dbiases += self.bias_regularizer_l1 * dL1
		# L2 on biases
		if self.bias_regularizer_l2 > 0:
			self.dbiases += 2 * self.bias_regularizer_l2 * \
			self.biases

		# Gradient on values
		self.dinputs = np.dot(dvalues, self.weights.T)
	# Retrieve layer parameters
	def get_parameters(self):
		return self.weights, self.biases
	# Set weights and biases in a layer instance
	def set_parameters(self, weights, biases):
		self.weights = weights
		self.biases = biases

# Dropout
class Layer_Dropout:
	# Init
	def __init__(self, rate):
		# Store rate, we invert it as for example for dropout
		# of 0.1 we need success rate of 0.9
		self.rate = 1 - rate
		# Forward pass
	def forward(self, inputs, training):
		# Save input values
		self.inputs = inputs
		# If not in the training mode - return values
		if not training:
			self.output = inputs.copy()
			return
		# Generate and save scaled mask
		self.binary_mask = np.random.binomial(1, self.rate,size=inputs.shape) / self.rate
		# Apply mask to output values
		self.output = inputs * self.binary_mask
		# Backward pass
	def backward(self, dvalues):
		# Gradient on values
		self.dinputs = dvalues * self.binary_mask

#Input "layer"
class Layer_Input:
	# Forward pass
	def forward(self, inputs, training):
		self.output = inputs

# ReLU activation
class Activation_ReLU:
	# Forward pass
	def forward(self, inputs, training):
		# Remember input values
		self.inputs = inputs
		# Calculate output values from inputs
		self.output = np.maximum(0, inputs)
	# Backward pass
	def backward(self, dvalues):
		# Since we need to modify original variable,
		# let's make a copy of values first
		self.dinputs = dvalues.copy()
		# Zero gradient where input values were negative
		self.dinputs[self.inputs <= 0] = 0
	# Calculate predictions for outputs
	def predictions(self, outputs):
		return outputs

# Softmax activation
class Activation_Softmax:
	# Forward pass
	def forward(self, inputs, training):
		# Remember input values
		self.inputs = inputs
		# Get unnormalized probabilities
		exp_values = np.exp(inputs - np.max(inputs, axis=1,keepdims=True))
		# Normalize them for each sample
		probabilities = exp_values / np.sum(exp_values, axis=1,keepdims=True)
		self.output = probabilities
	# Backward pass
	def backward(self, dvalues):
		# Create uninitialized array
		self.dinputs = np.empty_like(dvalues)
		# Enumerate outputs and gradients
		for index, (single_output, single_dvalues) in enumerate(zip(self.output, dvalues)):
			# Flatten output array
			single_output = single_output.reshape(-1, 1)
			# Calculate Jacobian matrix of the output
			jacobian_matrix = np.diagflat(single_output) - np.dot(single_output, single_output.T)
			# Calculate sample-wise gradient
			# and add it to the array of sample gradients
			self.dinputs[index] = np.dot(jacobian_matrix,single_dvalues)
	# Calculate predictions for outputs
	def predictions(self, outputs):
		return np.argmax(outputs, axis=1)

# Sigmoid activation
class Activation_Sigmoid:
	# Forward pass
	def forward(self, inputs, training):
		# Save input and calculate/save output
		# of the sigmoid function
		self.inputs = inputs
		self.output = 1 / (1 + np.exp(-inputs))
	# Backward pass
	def backward(self, dvalues):
		# Derivative - calculates from output of the sigmoid function
		self.dinputs = dvalues * (1 - self.output) * self.output
	# Calculate predictions for outputs
	def predictions(self, outputs):
		return (outputs > 0.5) * 1

# Linear activation
class Activation_Linear:
	# Forward pass
	def forward(self, inputs, training):
		# Just remember values
		self.inputs = inputs
		self.output = inputs
	# Backward pass
	def backward(self, dvalues):
		# derivative is 1, 1 * dvalues = dvalues - the chain rule
		self.dinputs = dvalues.copy()
	# Calculate predictions for outputs
	def predictions(self, outputs):
		return outputs

# SGD optimizer
class Optimizer_SGD:
	# Initialize optimizer - set settings,
	# learning rate of 1. is default for this optimizer
	def __init__(self, learning_rate=1., decay=0., momentum=0.):
		self.learning_rate = learning_rate
		self.current_learning_rate = learning_rate
		self.decay = decay
		self.iterations = 0
		self.momentum = momentum
	# Call once before any parameter updates
	def pre_update_params(self):
		if self.decay:
			self.current_learning_rate = self.learning_rate * (1. / (1. + self.decay * self.iterations))
	# Update parameters
	def update_params(self, layer):
		# If we use momentum
		if self.momentum:
			# If layer does not contain momentum arrays, create them
			# filled with zeros
			if not hasattr(layer, 'weight_momentums'):
				layer.weight_momentums = np.zeros_like(layer.weights)
				# If there is no momentum array for weights
				# The array doesn't exist for biases yet either.
				layer.bias_momentums = np.zeros_like(layer.biases)
			# Build weight updates with momentum - take previous
			# updates multiplied by retain factor and update with
			# current gradients
			weight_updates = self.momentum * layer.weight_momentums - self.current_learning_rate * layer.dweights
			layer.weight_momentums = weight_updates
			# Build bias updates
			bias_updates = self.momentum * layer.bias_momentums - self.current_learning_rate * layer.dbiases
			layer.bias_momentums = bias_updates
		# Vanilla SGD updates (as before momentum update)
		else:
			weight_updates = -self.current_learning_rate * layer.dweights
			bias_updates = -self.current_learning_rate * layer.dbiases
		# Update weights and biases using either
		# vanilla or momentum updates
		layer.weights += weight_updates
		layer.biases += bias_updates
	# Call once after any parameter updates
	def post_update_params(self):
		self.iterations += 1

# Adagrad optimizer
class Optimizer_Adagrad:
	# Initialize optimizer - set settings
	def __init__(self, learning_rate=1., decay=0., epsilon=1e-7):
		self.learning_rate = learning_rate
		self.current_learning_rate = learning_rate
		self.decay = decay
		self.iterations = 0
		self.epsilon = epsilon
	# Call once before any parameter updates
	def pre_update_params(self):
		if self.decay:
			self.current_learning_rate = self.learning_rate * (1. / (1. + self.decay * self.iterations))
	# Update parameters
	def update_params(self, layer):
		# If layer does not contain cache arrays,
		# create them filled with zeros
		if not hasattr(layer, 'weight_cache'):
			layer.weight_cache = np.zeros_like(layer.weights)
			layer.bias_cache = np.zeros_like(layer.biases)
		# Update cache with squared current gradients
		layer.weight_cache += layer.dweights**2
		layer.bias_cache += layer.dbiases**2
		# Vanilla SGD parameter update + normalization
		# with square rooted cache
		layer.weights += -self.current_learning_rate * layer.dweights / (np.sqrt(layer.weight_cache) + self.epsilon)
		layer.biases += -self.current_learning_rate * layer.dbiases / (np.sqrt(layer.bias_cache) + self.epsilon)
	# Call once after any parameter updates
	def post_update_params(self):
		self.iterations += 1

# RMSprop optimizer
class Optimizer_RMSprop:
	# Initialize optimizer - set settings
	def __init__(self, learning_rate=0.001, decay=0., epsilon=1e-7,
		rho=0.9):
		self.learning_rate = learning_rate
		self.current_learning_rate = learning_rate
		self.decay = decay
		self.iterations = 0
		self.epsilon = epsilon
		self.rho = rho
	# Call once before any parameter updates
	def pre_update_params(self):
		if self.decay:
			self.current_learning_rate = self.learning_rate * (1. / (1. + self.decay * self.iterations))
	# Update parameters
	def update_params(self, layer):
		# If layer does not contain cache arrays,
		# create them filled with zeros
		if not hasattr(layer, 'weight_cache'):
			layer.weight_cache = np.zeros_like(layer.weights)
			layer.bias_cache = np.zeros_like(layer.biases)
		# Update cache with squared current gradients
		layer.weight_cache = self.rho * layer.weight_cache + (1 - self.rho) * layer.dweights**2
		layer.bias_cache = self.rho * layer.bias_cache + (1 - self.rho) * layer.dbiases**2
		# Vanilla SGD parameter update + normalization
		# with square rooted cache
		layer.weights += -self.current_learning_rate * layer.dweights / (np.sqrt(layer.weight_cache) + self.epsilon)
		layer.biases += -self.current_learning_rate * layer.dbiases / (np.sqrt(layer.bias_cache) + self.epsilon)
	# Call once after any parameter updates
	def post_update_params(self):
		self.iterations += 1

# Adam optimizer
class Optimizer_Adam:
	# Initialize optimizer - set settings
	def __init__(self, learning_rate=0.001, decay=0., epsilon=1e-7,
		beta_1=0.9, beta_2=0.999):
		self.learning_rate = learning_rate
		self.current_learning_rate = learning_rate
		self.decay = decay
		self.iterations = 0
		self.epsilon = epsilon
		self.beta_1 = beta_1
		self.beta_2 = beta_2
	# Call once before any parameter updates
	def pre_update_params(self):
		if self.decay:
			self.current_learning_rate = self.learning_rate * (1. / (1. + self.decay * self.iterations))
	# Update parameters
	def update_params(self, layer):
		# If layer does not contain cache arrays,
		# create them filled with zeros
		if not hasattr(layer, 'weight_cache'):
			layer.weight_momentums = np.zeros_like(layer.weights)
			layer.weight_cache = np.zeros_like(layer.weights)
			layer.bias_momentums = np.zeros_like(layer.biases)
			layer.bias_cache = np.zeros_like(layer.biases)
		# Update momentum with current gradients
		layer.weight_momentums = self.beta_1 * layer.weight_momentums + (1 - self.beta_1) * layer.dweights
		layer.bias_momentums = self.beta_1 * layer.bias_momentums + (1 - self.beta_1) * layer.dbiases
		# Get corrected momentum
		# self.iteration is 0 at first pass
		# and we need to start with 1 here
		weight_momentums_corrected = layer.weight_momentums / (1 - self.beta_1 ** (self.iterations + 1))
		bias_momentums_corrected = layer.bias_momentums / (1 - self.beta_1 ** (self.iterations + 1))
		# Update cache with squared current gradients
		layer.weight_cache = self.beta_2 * layer.weight_cache + (1 - self.beta_2) * layer.dweights**2
		layer.bias_cache = self.beta_2 * layer.bias_cache + (1 - self.beta_2) * layer.dbiases**2
		# Get corrected cache
		weight_cache_corrected = layer.weight_cache / (1 - self.beta_2 ** (self.iterations + 1))
		bias_cache_corrected = layer.bias_cache / (1 - self.beta_2 ** (self.iterations + 1))
		# Vanilla SGD parameter update + normalization
		# with square rooted cache
		layer.weights += -self.current_learning_rate * weight_momentums_corrected / (np.sqrt(weight_cache_corrected) + self.epsilon)
		layer.biases += -self.current_learning_rate * bias_momentums_corrected / (np.sqrt(bias_cache_corrected) + self.epsilon)
	# Call once after any parameter updates
	def post_update_params(self):
		self.iterations += 1

# Common loss class
class Loss:
	# Regularization loss calculation
	def regularization_loss(self):
		# 0 by default
		regularization_loss = 0
		# Calculate regularization loss
		# iterate all trainable layers
		for layer in self.trainable_layers:
			# L1 regularization - weights
			# calculate only when factor greater than 0
			if layer.weight_regularizer_l1 > 0:
				regularization_loss += layer.weight_regularizer_l1 * np.sum(np.abs(layer.weights))
			# L2 regularization - weights
			if layer.weight_regularizer_l2 > 0:
				regularization_loss += layer.weight_regularizer_l2 * np.sum(layer.weights * layer.weights)
			# L1 regularization - biases
			# calculate only when factor greater than 0
			if layer.bias_regularizer_l1 > 0:
				regularization_loss += layer.bias_regularizer_l1 * np.sum(np.abs(layer.biases))
			# L2 regularization - biases
			if layer.bias_regularizer_l2 > 0:
				regularization_loss += layer.bias_regularizer_l2 * np.sum(layer.biases * layer.biases)
		return regularization_loss
	# Set/remember trainable layers
	def remember_trainable_layers(self, trainable_layers):
		self.trainable_layers = trainable_layers
	# Calculates the data and regularization losses
	# given model output and ground truth values
	def calculate(self, output, y, *, include_regularization=False):
		# Calculate sample losses
		sample_losses = self.forward(output, y)
		# Calculate mean loss
		data_loss = np.mean(sample_losses)
		# Add accumulated sum of losses and sample count
		self.accumulated_sum += np.sum(sample_losses)
		self.accumulated_count += len(sample_losses)
		# If just data loss - return it
		if not include_regularization:
			return data_loss
		# Return the data and regularization losses
		return data_loss, self.regularization_loss()
	# Calculates accumulated loss
	def calculate_accumulated(self, *, include_regularization=False):
		# Calculate mean loss
		data_loss = self.accumulated_sum / self.accumulated_count
		# If just data loss - return it
		if not include_regularization:
			return data_loss
		# Return the data and regularization losses
		return data_loss, self.regularization_loss()
	# Reset variables for accumulated loss
	def new_pass(self):
		self.accumulated_sum = 0
		self.accumulated_count = 0

# Cross-entropy loss
class Loss_CategoricalCrossentropy(Loss):
	# Forward pass
	def forward(self, y_pred, y_true):
		# Number of samples in a batch
		samples = len(y_pred)
		# Clip data to prevent division by 0
		# Clip both sides to not drag mean towards any value
		y_pred_clipped = np.clip(y_pred, 1e-7, 1 - 1e-7)
		# Probabilities for target values -
		# only if categorical labels
		if len(y_true.shape) == 1:
			correct_confidences = y_pred_clipped[range(samples),y_true]
		# Mask values - only for one-hot encoded labels
		elif len(y_true.shape) == 2:
			correct_confidences = np.sum(y_pred_clipped * y_true,axis=1)
		# Losses
		negative_log_likelihoods = -np.log(correct_confidences)
		return negative_log_likelihoods
	# Backward pass
	def backward(self, dvalues, y_true):
		# Number of samples
		samples = len(dvalues)
		# Number of labels in every sample
		# We'll use the first sample to count them
		labels = len(dvalues[0])
		# If labels are sparse, turn them into one-hot vector
		if len(y_true.shape) == 1:
			y_true = np.eye(labels)[y_true]
		# Calculate gradient
		self.dinputs = -y_true / dvalues
		# Normalize gradient
		self.dinputs = self.dinputs / samples

# Softmax classifier - combined Softmax activation
# and cross-entropy loss for faster backward step
class Activation_Softmax_Loss_CategoricalCrossentropy():
	# Backward pass
	def backward(self, dvalues, y_true):
		# Number of samples
		samples = len(dvalues)
		# If labels are one-hot encoded,
		# turn them into discrete values
		if len(y_true.shape) == 2:
			y_true = np.argmax(y_true, axis=1)
		# Copy so we can safely modify
		self.dinputs = dvalues.copy()
		# Calculate gradient
		self.dinputs[range(samples), y_true] -= 1
		# Normalize gradient
		self.dinputs = self.dinputs / samples

# Binary cross-entropy loss
class Loss_BinaryCrossentropy(Loss):
	# Forward pass
	def forward(self, y_pred, y_true):
		# Clip data to prevent division by 0
		# Clip both sides to not drag mean towards any value
		y_pred_clipped = np.clip(y_pred, 1e-7, 1 - 1e-7)
		# Calculate sample-wise loss
		sample_losses = -(y_true * np.log(y_pred_clipped) + (1 - y_true) * np.log(1 - y_pred_clipped))
		sample_losses = np.mean(sample_losses, axis=-1)
		# Return losses
		return sample_losses
	# Backward pass
	def backward(self, dvalues, y_true):
		# Number of samples
		samples = len(dvalues)
		# Number of outputs in every sample
		# We'll use the first sample to count them
		outputs = len(dvalues[0])
		# Clip data to prevent division by 0
		# Clip both sides to not drag mean towards any value
		clipped_dvalues = np.clip(dvalues, 1e-7, 1 - 1e-7)
		# Calculate gradient
		self.dinputs = -(y_true / clipped_dvalues - (1 - y_true) / (1 - clipped_dvalues)) / outputs
		# Normalize gradient
		self.dinputs = self.dinputs / samples

# Mean Squared Error loss
class Loss_MeanSquaredError(Loss): # L2 loss
	# Forward pass
	def forward(self, y_pred, y_true):
		# Calculate loss
		sample_losses = np.mean((y_true - y_pred)**2, axis=-1)
		# Return losses
		return sample_losses
	# Backward pass
	def backward(self, dvalues, y_true):
		# Number of samples
		samples = len(dvalues)
		# Number of outputs in every sample
		# We'll use the first sample to count them
		outputs = len(dvalues[0])
		# Gradient on values
		self.dinputs = -2 * (y_true - dvalues) / outputs
		# Normalize gradient
		self.dinputs = self.dinputs / samples

# Mean Absolute Error loss
class Loss_MeanAbsoluteError(Loss): # L1 loss
	def forward(self, y_pred, y_true):
		# Calculate loss
		sample_losses = np.mean(np.abs(y_true - y_pred), axis=-1)
		# Return losses
		return sample_losses
	# Backward pass
	def backward(self, dvalues, y_true):
		# Number of samples
		samples = len(dvalues)
		# Number of outputs in every sample
		# We'll use the first sample to count them
		outputs = len(dvalues[0])
		# Calculate gradient
		self.dinputs = np.sign(y_true - dvalues) / outputs
		# Normalize gradient
		self.dinputs = self.dinputs / samples

# Common accuracy class
class Accuracy:
	# Calculates an accuracy
	# given predictions and ground truth values
	def calculate(self, predictions, y):
		# Get comparison results
		comparisons = self.compare(predictions, y)
		# Calculate an accuracy
		accuracy = np.mean(comparisons)
		# Add accumulated sum of matching values and sample count
		self.accumulated_sum += np.sum(comparisons)
		self.accumulated_count += len(comparisons)
		# Return accuracy
		return accuracy
	# Calculates accumulated accuracy
	def calculate_accumulated(self):
		# Calculate an accuracy
		accuracy = self.accumulated_sum / self.accumulated_count
		# Return the data and regularization losses
		return accuracy
	# Reset variables for accumulated accuracy
	def new_pass(self):
		self.accumulated_sum = 0
		self.accumulated_count = 0

# Accuracy calculation for classification model
class Accuracy_Categorical(Accuracy):
	def __init__(self, *, binary=False):
		# Binary mode?
		self.binary = binary
	# No initialization is needed
	def init(self, y):
		pass
	# Compares predictions to the ground truth values
	def compare(self, predictions, y):
		if not self.binary and len(y.shape) == 2:
			y = np.argmax(y, axis=1)
		return predictions == y

# Accuracy calculation for regression model
class Accuracy_Regression(Accuracy):
	def __init__(self):
		# Create precision property
		self.precision = None
	# Calculates precision value
	# based on passed-in ground truth values
	def init(self, y, reinit=False):
		if self.precision is None or reinit:
			self.precision = np.std(y) / 250
	# Compares predictions to the ground truth values
	def compare(self, predictions, y):
		return np.absolute(predictions - y) < self.precision

# Model class
class Model:
	def __init__(self):
		# Create a list of network objects
		self.layers = []
		# Softmax classifier's output object
		self.softmax_classifier_output = None
	# Add objects to the model
	def add(self, layer):
		self.layers.append(layer)
		#
	# Set loss, optimizer and accuracy
	def set(self, *, loss=None, optimizer=None, accuracy=None):
		if loss is not None:
			self.loss = loss
		if optimizer is not None:
			self.optimizer = optimizer
		if accuracy is not None:
			self.accuracy = accuracy
	# Finalize the model
	def finalize(self):
		# Create and set the input layer
		self.input_layer = Layer_Input()
		# Count all the objects
		layer_count = len(self.layers)
		# Initialize a list containing trainable layers:
		self.trainable_layers = []
		# Iterate the objects
		for i in range(layer_count):
			# If it's the first layer,
			# the previous layer object is the input layer
			if i == 0:
				self.layers[i].prev = self.input_layer
				self.layers[i].next = self.layers[i+1]
			# All layers except for the first and the last
			elif i < layer_count - 1:
				self.layers[i].prev = self.layers[i-1]
				self.layers[i].next = self.layers[i+1]
			# The last layer - the next object is the loss
			# Also let's save aside the reference to the last object
			# whose output is the model's output
			else:
				self.layers[i].prev = self.layers[i-1]
				self.layers[i].next = self.loss
				self.output_layer_activation = self.layers[i]
			# If layer contains an attribute called "weights",
			# it's a trainable layer -
			# add it to the list of trainable layers
			# We don't need to check for biases -
			# checking for weights is enough
			if hasattr(self.layers[i], 'weights'):
				self.trainable_layers.append(self.layers[i])
		# Update loss object with trainable layers
		if self.loss is not None:
			self.loss.remember_trainable_layers(self.trainable_layers)
		# If output activation is Softmax and
		# loss function is Categorical Cross-Entropy
		# create an object of combined activation
		# and loss function containing
		# faster gradient calculation
		if isinstance(self.layers[-1], Activation_Softmax) and isinstance(self.loss, Loss_CategoricalCrossentropy):
			# Create an object of combined activation
			# and loss functions
			self.softmax_classifier_output = Activation_Softmax_Loss_CategoricalCrossentropy()
	# Train the model
	def train(self, X, y, *, epochs=1, batch_size=None,print_every=1, validation_data=None):
		# Initialize accuracy object
		self.accuracy.init(y)
		# Default value if batch size is not being set
		train_steps = 1
		# Calculate number of steps
		if batch_size is not None:
			train_steps = len(X) // batch_size
			# Dividing rounds down. If there are some remaining
			# data but not a full batch, this won't include it
			# Add `1` to include this not full batch
			if train_steps * batch_size < len(X):
				train_steps += 1
		# Main training loop
		for epoch in range(1, epochs+1):
			# Print epoch number
			print(f'epoch: {epoch}')
			# Reset accumulated values in loss and accuracy objects
			self.loss.new_pass()
			self.accuracy.new_pass()
			# Iterate over steps
			for step in range(train_steps):
				# If batch size is not set -
				# train using one step and full dataset
				if batch_size is None:
					batch_X = X
					batch_y = y
				# Otherwise slice a batch
				else:
						batch_X = X[step*batch_size:(step+1)*batch_size]
						atch_y = y[step*batch_size:(step+1)*batch_size]
				# Perform the forward pass
				output = self.forward(batch_X, training=True)
				# Calculate loss
				data_loss, regularization_loss = self.loss.calculate(output, batch_y,include_regularization=True)
				loss = data_loss + regularization_loss
				# Get predictions and calculate an accuracy
				predictions = self.output_layer_activation.predictions(output)
				accuracy = self.accuracy.calculate(predictions,batch_y)
				# Perform backward pass
				self.backward(output, batch_y)
				# Optimize (update parameters)
				self.optimizer.pre_update_params()
				for layer in self.trainable_layers:
					self.optimizer.update_params(layer)
				self.optimizer.post_update_params()
				# Print a summary
				if not step % print_every or step == train_steps - 1:
					print(f'step: {step}, ' +
						f'acc: {accuracy:.3f}, ' +
						f'loss: {loss:.3f} (' +
						f'data_loss: {data_loss:.3f}, ' +
						f'reg_loss: {regularization_loss:.3f}), ' +
						f'lr: {self.optimizer.current_learning_rate}')
						# Get and print epoch loss and accuracy
			epoch_data_loss, epoch_regularization_loss = self.loss.calculate_accumulated(include_regularization=True)
			epoch_loss = epoch_data_loss + epoch_regularization_loss
			epoch_accuracy = self.accuracy.calculate_accumulated()
			print(f'training, ' +
				f'acc: {epoch_accuracy:.3f}, ' +
				f'loss: {epoch_loss:.3f} (' +
				f'data_loss: {epoch_data_loss:.3f}, ' +
				f'reg_loss: {epoch_regularization_loss:.3f}), ' +
				f'lr: {self.optimizer.current_learning_rate}')
			# If there is the validation data
			if validation_data is not None:
				# Evaluate the model:
				self.evaluate(*validation_data,batch_size=batch_size)
	# Evaluates the model using passed-in dataset
	def evaluate(self, X_val, y_val, *, batch_size=None):
		# Default value if batch size is not being set
		validation_steps = 1
		# Calculate number of steps
		if batch_size is not None:
			validation_steps = len(X_val) // batch_size
			# Dividing rounds down. If there are some remaining
			# data but not a full batch, this won't include it
			# Add `1` to include this not full batch
			if validation_steps * batch_size < len(X_val):
				validation_steps += 1
		# Reset accumulated values in loss
		# and accuracy objects
		self.loss.new_pass()
		self.accuracy.new_pass()
		# Iterate over steps
		for step in range(validation_steps):
			# If batch size is not set -
			# train using one step and full dataset
			if batch_size is None:
				batch_X = X_val
				batch_y = y_val
			# Otherwise slice a batch
			else:
				batch_X = X_val[step*batch_size:(step+1)*batch_size]
				batch_y = y_val[step*batch_size:(step+1)*batch_size]
			# Perform the forward pass
			output = self.forward(batch_X, training=False)
			# Calculate the loss
			self.loss.calculate(output, batch_y)
			# Get predictions and calculate an accuracy
			predictions = self.output_layer_activation.predictions(output)
			self.accuracy.calculate(predictions, batch_y)
		# Get and print validation loss and accuracy
		validation_loss = self.loss.calculate_accumulated()
		validation_accuracy = self.accuracy.calculate_accumulated()
		# Print a summary
		print(f'validation, ' +
			f'acc: {validation_accuracy:.3f}, ' +
			f'loss: {validation_loss:.3f}')
	# Predicts on the samples
	def predict(self, X, *, batch_size=None):
		# Default value if batch size is not being set
		prediction_steps = 1
		# Calculate number of steps
		if batch_size is not None:
			prediction_steps = len(X) // batch_size
			# Dividing rounds down. If there are some remaining
			# data but not a full batch, this won't include it
			# Add `1` to include this not full batch
			if prediction_steps * batch_size < len(X):
				prediction_steps += 1
		# Model outputs
		output = []
		# Iterate over steps
		for step in range(prediction_steps):
			# If batch size is not set -
			# train using one step and full dataset
			if batch_size is None:
				batch_X = X
			# Otherwise slice a batch
			else:
				batch_X = X[step*batch_size:(step+1)*batch_size]
			# Perform the forward pass
			batch_output = self.forward(batch_X, training=False)
			# Append batch prediction to the list of predictions
			output.append(batch_output)
		# Stack and return results
		return np.vstack(output)
	# Performs forward pass
	def forward(self, X, training):
		# Call forward method on the input layer
		# this will set the output property that
		# the first layer in "prev" object is expecting
		self.input_layer.forward(X, training)
		# Call forward method of every object in a chain
		# Pass output of the previous object as a parameter
		for layer in self.layers:
			layer.forward(layer.prev.output, training)
		# "layer" is now the last object from the list,
		# return its output
	# Performs backward pass
	def backward(self, output, y):
		# If softmax classifier
		if self.softmax_classifier_output is not None:
			# First call backward method
			# on the combined activation/loss
			# this will set dinputs property
			self.softmax_classifier_output.backward(output, y)
			# Since we'll not call backward method of the last layer
			# which is Softmax activation
			# as we used combined activation/loss
			# object, let's set dinputs in this object
			self.layers[-1].dinputs = self.softmax_classifier_output.dinputs
			# Call backward method going through
			# all the objects but last
			# in reversed order passing dinputs as a parameter
			for layer in reversed(self.layers[:-1]):
				layer.backward(layer.next.dinputs)
			return
		# First call backward method on the loss
		# this will set dinputs property that the last
		# layer will try to access shortly
		self.loss.backward(output, y)
		# Call backward method going through all the objects
		# in reversed order passing dinputs as a parameter
		for layer in reversed(self.layers):
			layer.backward(layer.next.dinputs)
	# Retrieves and returns parameters of trainable layers
	def get_parameters(self):
		# Create a list for parameters
		parameters = []
		# Iterable trainable layers and get their parameters
		for layer in self.trainable_layers:
			parameters.append(layer.get_parameters())
		# Return a list
		return parameters
	#Updates the model with new parameters
	def set_parameters(self, parameters):
		# Iterate over the parameters and layers
		# and update each layers with each set of the parameters
		for parameter_set, layer in zip(parameters,self.trainable_layers):
			layer.set_parameters(*parameter_set)
	# Saves the parameters to a file
	def save_parameters(self, path):
		# Open a file in the binary-write mode
		# and save parameters into it
		with open(path, 'wb') as f:
			pickle.dump(self.get_parameters(), f)
	# Loads the weights and updates a model instance with them
	def load_parameters(self, path):
		# Open file in the binary-read mode,
		# load weights and update trainable layers
		with open(path, 'rb') as f:
			self.set_parameters(pickle.load(f))
	# Saves the model
	def save(self, path):
		# Make a deep copy of current model instance
		model = copy.deepcopy(self)
		# Reset accumulated values in loss and accuracy objects
		model.loss.new_pass()
		model.accuracy.new_pass()
		# Remove data from the input layer
		# and gradients from the loss object
		model.input_layer.__dict__.pop('output', None)
		model.loss.__dict__.pop('dinputs', None)
		# For each layer remove inputs, output and dinputs properties
		for layer in model.layers:
			for property in ['inputs', 'output', 'dinputs','dweights', 'dbiases']:
				layer.__dict__.pop(property, None)
		# Open a file in the binary-write mode and save the model
		with open(path, 'wb') as f:
			pickle.dump(model, f)
	# Loads and returns a model
	@staticmethod
	def load(path):
		# Open file in the binary-read mode, load a model
		with open(path, 'rb') as f:
			model = pickle.load(f)
		# Return a model
		return model

# Loads a MNIST dataset
def load_mnist_dataset(dataset, path):
	# Scan all the directories and create a list of labels
	labels = os.listdir(os.path.join(path, dataset))
	# Create lists for samples and labels
	X = []
	y = []
	# For each label folder
	for label in labels:
		# And for each image in given folder
		for file in os.listdir(os.path.join(path, dataset, label)):
			# Read the image
			image = cv2.imread(os.path.join(path, dataset, label, file),cv2.IMREAD_UNCHANGED)
		# And append it and a label to the lists
		X.append(image)
		y.append(label)
	# Convert the data to proper numpy arrays and return
	return np.array(X), np.array(y).astype('uint8')

# MNIST dataset (train + test)
def create_data_mnist(path):
	# Load both sets separately
	X, y = load_mnist_dataset('train', path)
	X_test, y_test = load_mnist_dataset('test', path)
	# And return all the data
	return X, y, X_test, y_test

# Create dataset
X, y = emnist.extract_training_samples('digits')
X_test, y_test = emnist.extract_test_samples('digits')

# Instantiate the model
model = Model()

# Add layers
model.add(Layer_Dense(2, 512, weight_regularizer_l2=5e-4,bias_regularizer_l2=5e-4))
model.add(Activation_ReLU())
model.add(Layer_Dropout(0.1))
model.add(Layer_Dense(512, 3))
model.add(Activation_Softmax())

# Set loss, optimizer and accuracy objects
model.set(
	loss=Loss_CategoricalCrossentropy(),
	optimizer=Optimizer_Adam(learning_rate=0.05, decay=5e-5),
	accuracy=Accuracy_Categorical()
)

# Finalize the model
model.finalize()

# Train the model
model.train(X, y, validation_data=(X_test, y_test),epochs=10000, print_every=100)

"""
# Predict on the image
confidences = model.predict(image_data)
# Get prediction instead of confidence levels
predictions = model.output_layer_activation.predictions(confidences)
# Get label name from label index
prediction = fashion_mnist_labels[predictions[0]]
print(prediction)
"""