ValueError: shapes (240000,28,28) and (2,512) not aligned: 28 (dim 2) != 2 (dim 0)

I’m making a CNN and I’ve got this error that the matrices don’t align and i understand the error but i don’t know how to fix it. Here is the code:

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
        # 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)
# 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
# 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
# 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
        
# 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)

And this is the error i get in sublime text:

epoch: 1
Traceback (most recent call last):
  File "/media/luke/New Volume/final project/untitled.py", line 654, in <module>
    model.train(X, y, validation_data=(X_test, y_test),epochs=10000, print_every=100)
  File "/media/luke/New Volume/final project/untitled.py", line 430, in train
    output = self.forward(batch_X, training=True)
  File "/media/luke/New Volume/final project/untitled.py", line 545, in forward
    layer.forward(layer.prev.output, training)
  File "/media/luke/New Volume/final project/untitled.py", line 29, in forward
    self.output = np.dot(inputs, self.weights) + self.biases
  File "/home/luke/.local/lib/python3.8/site-packages/nnfs/core.py", line 22, in dot
    return orig_dot(*[a.astype('float64') for a in args], **kwargs).astype('float32')
  File "<__array_function__ internals>", line 5, in dot
ValueError: shapes (240000,28,28) and (2,512) not aligned: 28 (dim 2) != 2 (dim 0)

As you can see it gets to epoch 1 then when trying to do the numpy dot product and then cant do it.

I’d appreciate any help

Thanks :)

Answer

Firstly, you should flatten your input so its shape is (240000, 28*28) = (240000, 784). After that, the problem is in this line:

model.add(Layer_Dense(2, 512, weight_regularizer_l2=5e-4,bias_regularizer_l2=5e-4))

You set your input size to 2, when it should be 784 which is the number of pixels in each image (assuming you’re using MNIST).

model.add(Layer_Dense(784, 512, weight_regularizer_l2=5e-4,bias_regularizer_l2=5e-4))

Should work correctly if your inputs are flattened.

Edit: To flatten your inputs I would use np.reshape as demonstrated here https://stackoverflow.com/a/18758049/11777402.

X.reshape(240000, 784)