[MRG]using mini batch for gd

fromscratchtoml/neural_network/activations.py

@@ -141,10 +141,15 @@ def softmax(x, return_deriv=False):
         -------
         numpy.ndarray : softmax of x
         """
+        # shifting for numerical stability
+        # refer https://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative
+
+        x -= np.max(x, axis=-1, keepdims=True)
         n = np.exp(x)
-        d = np.sum(np.exp(x))
+        d = np.sum(n, axis=-1, keepdims=True)
+        _softmax = n / d
 
         if return_deriv:
-            return n / d, 1 - (n / d)
+            return _softmax, _softmax * (1 - _softmax)
 
-        return n / d
+        return _softmax

fromscratchtoml/neural_network/layers/activation.py

@@ -76,20 +76,19 @@ def forward(self, X, return_deriv=False):
 
         return self.output
 
-    def back_propogate(self, delta):
+    def back_propogate(self, dEdO):
         """
         Backpropogate the error, this function adds the share of activation layer to the accumulated delta.
 
         Parameters
         ----------
-        delta : numpy.ndarray
+        dEdO : numpy.ndarray
             The accumulated delta used for calculating error gradient with respect to parameters.
 
         Returns
         -------
         numpy.array : The accumulated delta.
-        numpy.array : Current updated derivative of error with respect to bias.
-        numpy.array : Current updated derivative of error with respect to weight.
         """
-        delta = delta * self.output_deriv
-        return delta, 0, 0
+
+        dEdO = dEdO * self.output_deriv
+        return dEdO

fromscratchtoml/neural_network/layers/base_layer.py

@@ -33,20 +33,14 @@ class Layer(object):
     >>> model.fit(X1, y1, batch_size=4, epochs=100)
     >>> model.predict(X1, one_hot=True)
     """
-    def optimize(self, optimizer, der_cost_bias, der_cost_weight):
+    def optimize(self, optimizer):
         """
         Optimize the weights corresponding to the optimizer function supplied.
 
         Parameters
         ----------
         optimizer : fromscratchtoml.neural_network.optimizers
             The optimizing procedure followed for updating the weights.
-        der_cost_bias : numpy.ndarray
-            The derivative of error with respect to bias.
-        der_cost_weights : numpy.ndarray
-            The derivative of error with respect to weights.
         """
-        if self.trainable:
-            self.weights = optimizer.update_weights(self.weights, der_cost_weight)
-            self.biases = optimizer.update_weights(self.biases, der_cost_bias)
+        pass
         return

fromscratchtoml/neural_network/layers/dense.py

@@ -86,17 +86,17 @@ def forward(self, X, return_deriv=False):
 
         if self.weights is None:
             self.biases = np.random.randn(1, self.units)
-            self.weights = np.random.randn(X.shape[0], self.units)
+            self.weights = np.random.randn(X.shape[1], self.units)
 
         self.input = X
-        self.output = (np.dot(X.T, self.weights) + self.biases).T
+        self.output = np.dot(X, self.weights) + self.biases
 
         if return_deriv:
             return self.output, 0
 
         return self.output
 
-    def back_propogate(self, delta):
+    def back_propogate(self, dEdO):
         """
         Backpropogate the error, this function adds the share of dense layer to the accumulated delta.
 
@@ -108,10 +108,13 @@ def back_propogate(self, delta):
         Returns
         -------
         numpy.array : The accumulated delta.
-        numpy.array : Current updated derivative of error with respect to bias.
-        numpy.array : Current updated derivative of error with respect to weight.
         """
-        der_error_bias = delta.T
-        der_error_weight = np.dot(delta, self.input.T).T
-        delta = np.dot(self.weights, delta)
-        return delta, der_error_bias, der_error_weight
+
+        self.dEdB = np.sum(dEdO)
+        self.dEdW = np.dot(self.input.T, dEdO)
+        dEdO = np.dot(dEdO, self.weights.T)
+        return dEdO
+
+    def optimize(self, optimizer):
+        self.weights = optimizer.update_weights(self.weights, self.dEdW)
+        self.biases = optimizer.update_weights(self.biases, self.dEdB)

fromscratchtoml/neural_network/models/sequential.py

@@ -39,7 +39,7 @@ class Sequential(BaseModel):
     >>> sgd = StochasticGradientDescent(learning_rate=0.1)
     >>> model.compile(optimizer=sgd, loss="mean_squared_error")
     >>> model.fit(X1, y1, batch_size=4, epochs=100)
-    >>> model.predict(X1, one_hot=True)
+    >>> model.predict(X1)
     """
     def __init__(self, verbose=False, vis_each_epoch=False):
         """
@@ -86,7 +86,11 @@ def accuracy(self, X, y):
         -------
         float : The accuracy in percentage.
         """
-        y_pred = self.predict(X, one_hot=True)
+
+        if len(y.shape) > 1:
+            y = np.argmax(y, axis=1)
+        y_pred = self.predict(X)
+
         diff_arr = y - y_pred
         total_samples = y.shape[0]
 
@@ -118,7 +122,7 @@ def fit(self, X, y, epochs, batch_size=None):
                 self.__update_batch(batch_X, batch_y)
 
             if self.verbose or epoch == epochs - 1:
-                y_pred = self.predict(X, one_hot=True)
+                y_pred = self.predict(X, prob=True)
                 loss = self.loss(y_pred, y)
                 acc = self.accuracy(X, y)
                 print("\nepoch: {}/{} ".format(epoch + 1, epochs), end="")
@@ -138,66 +142,35 @@ def __update_batch(self, X, Y):
         Y : numpy.ndarray
             The corresponding label to the input.
         """
-        # TODO write mini batch SGD
-        # der_error_bias = None
-        # der_error_weight = None
-
-        for x, y in zip(X, Y):
-            delta_der_error_bias, delta_der_error_weight = self.back_propogation(x, y)
-            # if der_error_bias is None:
-            #     der_error_bias, der_error_weight = delta_der_error_bias, delta_der_error_weight
-            # else:
-            #     der_error_bias += delta_der_error_bias
-            #     der_error_weight += delta_der_error_weight
-
-        # updates weights in each layer
-        # for layer, db, dw in zip(self.layers, der_error_bias, der_error_weight):
-        #     layer.optimize(self.optimizer, db, dw)
-
-    def back_propogation(self, x, y):
+        y_pred = self.forwardpass(X)
+
+        _, dEdO = self.loss(y_pred, Y, return_deriv=True)
+
+        self.back_propogate_and_update(dEdO, self.optimizer)
+
+    def back_propogate_and_update(self, dEdO, optimizer):
         """
         Backpropogate the error from the last layer to the first and then optimize the weights.
 
         Parameters
         ----------
-        x : numpy.ndarray
-            The input to the model.
-        y : numpy.ndarray
-            The corresponding label to the input.
-
-        Returns
-        -------
-        numpy.array : The derivative of error with respect to biases.
-        numpy.array : The derivative of error with respect to weights.
+        dEdO : numpy.ndarray
+            The accumulated delta used for calculating error gradient with respect to parameters.
+        optimizer : fromscratchtoml.neural_network.optimizers
+            The optimizing procedure followed for updating the weights.
         """
-        y_pred, y_pred_deriv = self.forwardpass(x, return_deriv=True)
-
-        loss, loss_grad = self.loss(y_pred, y, return_deriv=True)
-
-        delta = loss_grad
-
-        der_error_biases = []
-        der_error_weights = []
 
         for layer in reversed(self.layers):
-            # updates delta
-            delta, der_error_bias, der_error_weight = layer.back_propogate(delta)
-
-            if hasattr(layer, 'weights'):
-                layer.optimize(self.optimizer, der_error_bias, der_error_weight)
+            dEdO = layer.back_propogate(dEdO)
+            layer.optimize(optimizer)
 
-            der_error_biases.append(der_error_bias)
-            der_error_weights.append(der_error_weight)
-
-        return np.array(der_error_biases[::-1]), np.array(der_error_weights[::-1])
-
-    def forwardpass(self, x, return_deriv=False):
+    def forwardpass(self, X, return_deriv=False):
         """
         Forward pass the input through all the layers in the current model.
 
         Parameters
         ----------
-        x : numpy.ndarray
+        X : numpy.ndarray
             The input to the model.
         return_deriv : bool, optional
             If set to true, the function returns derivative of the output along with the output.
@@ -206,45 +179,37 @@ def forwardpass(self, x, return_deriv=False):
         -------
         numpy.array : The output of the model.
         """
-        z = x
+        Z = X
 
         for layer in self.layers:
-            z, z_deriv = layer.forward(z, return_deriv=True)
+            Z, Z_deriv = layer.forward(Z, return_deriv=True)
 
         if return_deriv:
-            return z, z_deriv
+            return Z, Z_deriv
 
-        return z
+        return Z
 
-    def predict(self, X, one_hot=False):
+    def predict(self, X, prob=False):
         """
         Predicts the ouput of the model based on the trained parameters.
 
         Parameters
         ----------
         X : numpy.ndarray
             The input to be predicted.
-        one_hot : bool, optional
-            If set to true, it returns output in one hot fashion instead of a single label.
+        prob : bool, optional
+            If set to true, it returns output probabilities of each class.
 
         Returns
         -------
         numpy.array : The prediction.
         """
-        Z = []
-        for x in X:
-            z = self.forwardpass(x)
-
-            t = np.zeros_like(z)
-            if one_hot:
-                # returns one hot
-                t[np.argmax(z)] = 1
-                Z.append(t.flatten())
-            else:
-                # returns class
-                Z.append(np.argmax(z))
-
-        return np.array(Z)
+        predictions = self.forwardpass(X)
+
+        if prob is False:
+            predictions = np.argmax(predictions, axis=1)
+
+        return predictions
 
     def add(self, layer):
         """

fromscratchtoml/test/neural_network/test_neural_network.py

@@ -31,7 +31,7 @@ def setUp(self):
         X22 = Distribution.radial_binary(pts=300,
                        mean=[0, 0],
                        st=4,
-                       ed=5, seed=20)
+                       ed=5, seed=10)
 
         Y11 = np.ones(X11.shape[0])
         Y22 = np.zeros(X11.shape[0])
@@ -52,9 +52,6 @@ def test_dense_acts_sgd(self):
         model.add(Dense(2, seed=7))
         model.add(Activation('tanh'))
 
-        model.add(Dense(2, seed=5))
-        model.add(Activation('softmax'))
-
         model.add(Dense(2, seed=2))
         model.add(Activation('relu'))
 
@@ -65,18 +62,19 @@ def test_dense_acts_sgd(self):
         model.add(Activation('linear'))
 
         model.add(Dense(2, seed=6))
+        model.add(Activation('softmax'))
 
         sgd = StochasticGradientDescent(learning_rate=0.05)
         model.compile(optimizer=sgd, loss="mean_squared_error")
 
-        model.fit(self.X_train, self.y_train, epochs=14)
+        model.fit(self.X_train, self.y_train, epochs=14, batch_size=4)
 
-        expected_biases = np.array([[0.08650937, 1.00013189]], dtype=np.float128)
-        self.assertTrue(np.allclose(expected_biases, model.layers[-1].biases))
+        expected_biases = np.array([[1.38503523, -0.51962709]], dtype=np.float128)
+        self.assertTrue(np.allclose(expected_biases, model.layers[-2].biases))
 
-        expected_weights = np.array([[-0.49908263, -0.17316507], [-0.42623203, 0.48448988]], dtype=np.float128)
-        self.assertTrue(np.allclose(expected_weights, model.layers[-1].weights))
+        expected_weights = np.array([[-1.31788536, 1.49334281], [-0.10027775, -1.39507145]], dtype=np.float128)
+        self.assertTrue(np.allclose(expected_weights, model.layers[-2].weights))
 
-        predictions = model.predict(self.X_test, one_hot=1)
+        predictions = model.predict(self.X_test)
 
-        self.assertTrue(np.allclose(predictions, self.y_test))
+        self.assertTrue(np.allclose((predictions), np.argmax(self.y_test, axis=1)))