diff --git a/CMakeLists.txt b/CMakeLists.txt
index 868c899c..59f86fff 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -50,6 +50,7 @@ add_library(neural-fortran
   src/nf/nf_loss_submodule.f90
   src/nf/nf_maxpool2d_layer.f90
   src/nf/nf_maxpool2d_layer_submodule.f90
+  src/nf/nf_metrics.f90
   src/nf/nf_network.f90
   src/nf/nf_network_submodule.f90
   src/nf/nf_optimizers.f90
diff --git a/README.md b/README.md
index f8b2174c..054582b2 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
 * Stochastic gradient descent optimizers: Classic, momentum, Nesterov momentum,
   RMSProp, Adagrad, Adam, AdamW
 * More than a dozen activation functions and their derivatives
+* Loss functions and metrics: Quadratic, Mean Squared Error, Pearson Correlation etc.
 * Loading dense and convolutional models from Keras HDF5 (.h5) files
 * Data-based parallelism
 
diff --git a/example/cnn_mnist.f90 b/example/cnn_mnist.f90
index e8f6f0a0..fecb7deb 100644
--- a/example/cnn_mnist.f90
+++ b/example/cnn_mnist.f90
@@ -11,9 +11,6 @@ program cnn_mnist
   real, allocatable :: training_images(:,:), training_labels(:)
   real, allocatable :: validation_images(:,:), validation_labels(:)
   real, allocatable :: testing_images(:,:), testing_labels(:)
-  real, allocatable :: input_reshaped(:,:,:,:)
-  real :: acc
-  logical :: ok
   integer :: n
   integer, parameter :: num_epochs = 10
 
diff --git a/example/dense_mnist.f90 b/example/dense_mnist.f90
index 8a00c08d..3b0ec07d 100644
--- a/example/dense_mnist.f90
+++ b/example/dense_mnist.f90
@@ -1,6 +1,6 @@
 program dense_mnist
 
-  use nf, only: dense, input, network, sgd, label_digits, load_mnist
+  use nf, only: dense, input, network, sgd, label_digits, load_mnist, corr
 
   implicit none
 
@@ -38,9 +38,17 @@ program dense_mnist
       optimizer=sgd(learning_rate=3.) &
     )
 
-    if (this_image() == 1) &
-      print '(a,i2,a,f5.2,a)', 'Epoch ', n, ' done, Accuracy: ', accuracy( &
-        net, validation_images, label_digits(validation_labels)) * 100, ' %'
+    block
+      real, allocatable :: output_metrics(:,:)
+      real, allocatable :: mean_metrics(:)
+      ! 2 metrics; 1st is default loss function (quadratic), other is Pearson corr.
+      output_metrics = net % evaluate(validation_images, label_digits(validation_labels), metric=corr())
+      mean_metrics = sum(output_metrics, 1) / size(output_metrics, 1)
+      if (this_image() == 1) &
+        print '(a,i2,3(a,f6.3))', 'Epoch ', n, ' done, Accuracy: ', &
+          accuracy(net, validation_images, label_digits(validation_labels)) * 100, &
+          '%, Loss: ', mean_metrics(1), ', Pearson correlation: ', mean_metrics(2)
+    end block
 
   end do epochs
 
diff --git a/example/get_set_network_params.f90 b/example/get_set_network_params.f90
index c4a5f980..bf9613b6 100644
--- a/example/get_set_network_params.f90
+++ b/example/get_set_network_params.f90
@@ -9,8 +9,7 @@ program get_set_network_params
   integer, parameter :: test_size = 30
   real :: xtest(test_size), ytest(test_size)
   real :: ypred1(test_size), ypred2(test_size)
-  integer :: i, n, nparam
-  real, allocatable :: parameters(:)
+  integer :: i, n
 
   print '("Getting and setting network parameters")'
   print '(60("="))'
diff --git a/example/quadratic.f90 b/example/quadratic.f90
index 9bd4cdc3..30303ad8 100644
--- a/example/quadratic.f90
+++ b/example/quadratic.f90
@@ -24,7 +24,7 @@ program quadratic_fit
   real, allocatable :: x(:), y(:) ! training data
   real, allocatable :: xtest(:), ytest(:) ! testing data
 
-  integer :: i, n
+  integer :: i
 
   print '("Fitting quadratic function")'
   print '(60("="))'
@@ -277,7 +277,7 @@ subroutine rmsprop_optimizer( &
     real, intent(in) :: xtest(:), ytest(:)
     real, intent(in) :: learning_rate, decay_rate
     integer, intent(in) :: num_epochs
-    integer :: i, j, n
+    integer :: i, n
     real, allocatable :: ypred(:)
 
     print '(a)', 'RMSProp optimizer'
@@ -446,4 +446,4 @@ subroutine shuffle(arr)
     end do
   end subroutine shuffle
 
-end program quadratic_fit
\ No newline at end of file
+end program quadratic_fit
diff --git a/fpm.toml b/fpm.toml
index 4fc21b34..b638d215 100644
--- a/fpm.toml
+++ b/fpm.toml
@@ -1,5 +1,5 @@
 name = "neural-fortran"
-version = "0.16.1"
+version = "0.17.0"
 license = "MIT"
 author = "Milan Curcic"
 maintainer = "milancurcic@hey.com"
diff --git a/src/nf.f90 b/src/nf.f90
index ae72e1e2..b97d9e62 100644
--- a/src/nf.f90
+++ b/src/nf.f90
@@ -5,6 +5,7 @@ module nf
   use nf_layer_constructors, only: &
     conv2d, dense, flatten, input, maxpool2d, reshape
   use nf_loss, only: mse, quadratic
+  use nf_metrics, only: corr, maxabs
   use nf_network, only: network
   use nf_optimizers, only: sgd, rmsprop, adam, adagrad
   use nf_activation, only: activation_function, elu, exponential,  &
diff --git a/src/nf/nf_conv2d_layer.f90 b/src/nf/nf_conv2d_layer.f90
index 7b72980c..2f286030 100644
--- a/src/nf/nf_conv2d_layer.f90
+++ b/src/nf/nf_conv2d_layer.f90
@@ -89,19 +89,19 @@ pure module function get_num_params(self) result(num_params)
         !! Number of parameters
     end function get_num_params
 
-    pure module function get_params(self) result(params)
+    module function get_params(self) result(params)
       !! Return the parameters (weights and biases) of this layer.
       !! The parameters are ordered as weights first, biases second.
-      class(conv2d_layer), intent(in) :: self
+      class(conv2d_layer), intent(in), target :: self
         !! A `conv2d_layer` instance
       real, allocatable :: params(:)
         !! Parameters to get
     end function get_params
 
-    pure module function get_gradients(self) result(gradients)
+    module function get_gradients(self) result(gradients)
       !! Return the gradients of this layer.
       !! The gradients are ordered as weights first, biases second.
-      class(conv2d_layer), intent(in) :: self
+      class(conv2d_layer), intent(in), target :: self
         !! A `conv2d_layer` instance
       real, allocatable :: gradients(:)
         !! Gradients to get
diff --git a/src/nf/nf_conv2d_layer_submodule.f90 b/src/nf/nf_conv2d_layer_submodule.f90
index b4804243..5f71084b 100644
--- a/src/nf/nf_conv2d_layer_submodule.f90
+++ b/src/nf/nf_conv2d_layer_submodule.f90
@@ -189,24 +189,32 @@ pure module function get_num_params(self) result(num_params)
   end function get_num_params
 
 
-  pure module function get_params(self) result(params)
-    class(conv2d_layer), intent(in) :: self
+  module function get_params(self) result(params)
+    class(conv2d_layer), intent(in), target :: self
     real, allocatable :: params(:)
 
+    real, pointer :: w_(:) => null()
+
+    w_(1:size(self % kernel)) => self % kernel
+
     params = [ &
-      pack(self % kernel, .true.), &
+      w_, &
       self % biases &
     ]
 
   end function get_params
 
 
-  pure module function get_gradients(self) result(gradients)
-    class(conv2d_layer), intent(in) :: self
+  module function get_gradients(self) result(gradients)
+    class(conv2d_layer), intent(in), target :: self
     real, allocatable :: gradients(:)
 
+    real, pointer :: dw_(:) => null()
+
+    dw_(1:size(self % dw)) => self % dw
+
     gradients = [ &
-      pack(self % dw, .true.), &
+      dw_, &
       self % db &
     ]
 
@@ -219,7 +227,7 @@ module subroutine set_params(self, params)
 
     ! Check that the number of parameters is correct.
     if (size(params) /= self % get_num_params()) then
-       error stop 'conv2d % set_params: Number of parameters does not match'
+      error stop 'conv2d % set_params: Number of parameters does not match'
     end if
 
     ! Reshape the kernel.
@@ -229,10 +237,9 @@ module subroutine set_params(self, params)
     )
 
     ! Reshape the biases.
-    self % biases = reshape( &
-      params(product(shape(self % kernel)) + 1:), &
-      [self % filters] &
-    )
+    associate(n => product(shape(self % kernel)))
+      self % biases = params(n + 1 : n + self % filters)
+    end associate
 
   end subroutine set_params
 
diff --git a/src/nf/nf_dense_layer.f90 b/src/nf/nf_dense_layer.f90
index ae523ccb..c5735799 100644
--- a/src/nf/nf_dense_layer.f90
+++ b/src/nf/nf_dense_layer.f90
@@ -87,19 +87,19 @@ pure module function get_num_params(self) result(num_params)
          !! Number of parameters in this layer
     end function get_num_params
 
-    pure module function get_params(self) result(params)
+    module function get_params(self) result(params)
       !! Return the parameters (weights and biases) of this layer.
       !! The parameters are ordered as weights first, biases second.
-      class(dense_layer), intent(in) :: self
+      class(dense_layer), intent(in), target :: self
         !! Dense layer instance
       real, allocatable :: params(:)
         !! Parameters of this layer
     end function get_params
 
-    pure module function get_gradients(self) result(gradients)
+    module function get_gradients(self) result(gradients)
       !! Return the gradients of this layer.
       !! The gradients are ordered as weights first, biases second.
-      class(dense_layer), intent(in) :: self
+      class(dense_layer), intent(in), target :: self
         !! Dense layer instance
       real, allocatable :: gradients(:)
         !! Gradients of this layer
@@ -110,7 +110,7 @@ module subroutine set_params(self, params)
       !! The parameters are ordered as weights first, biases second.
       class(dense_layer), intent(in out) :: self
         !! Dense layer instance
-      real, intent(in) :: params(:)
+      real, intent(in), target :: params(:)
         !! Parameters of this layer
     end subroutine set_params
 
diff --git a/src/nf/nf_dense_layer_submodule.f90 b/src/nf/nf_dense_layer_submodule.f90
index 4be23e33..50d5b10d 100644
--- a/src/nf/nf_dense_layer_submodule.f90
+++ b/src/nf/nf_dense_layer_submodule.f90
@@ -61,24 +61,32 @@ pure module function get_num_params(self) result(num_params)
   end function get_num_params
 
 
-  pure module function get_params(self) result(params)
-    class(dense_layer), intent(in) :: self
+  module function get_params(self) result(params)
+    class(dense_layer), intent(in), target :: self
     real, allocatable :: params(:)
 
+    real, pointer :: w_(:) => null()
+
+    w_(1:size(self % weights)) => self % weights
+
     params = [ &
-      pack(self % weights, .true.), &
+      w_, &
       self % biases &
     ]
 
   end function get_params
 
 
-  pure module function get_gradients(self) result(gradients)
-    class(dense_layer), intent(in) :: self
+  module function get_gradients(self) result(gradients)
+    class(dense_layer), intent(in), target :: self
     real, allocatable :: gradients(:)
 
+    real, pointer :: dw_(:) => null()
+
+    dw_(1:size(self % dw)) => self % dw
+
     gradients = [ &
-      pack(self % dw, .true.), &
+      dw_, &
       self % db &
     ]
 
@@ -87,24 +95,23 @@ end function get_gradients
 
   module subroutine set_params(self, params)
     class(dense_layer), intent(in out) :: self
-    real, intent(in) :: params(:)
+    real, intent(in), target :: params(:)
+
+    real, pointer :: p_(:,:) => null()
 
     ! check if the number of parameters is correct
     if (size(params) /= self % get_num_params()) then
       error stop 'Error: number of parameters does not match'
     end if
 
-    ! reshape the weights
-    self % weights = reshape( &
-      params(:self % input_size * self % output_size), &
-      [self % input_size, self % output_size] &
-    )
-
-    ! reshape the biases
-    self % biases = reshape( &
-      params(self % input_size * self % output_size + 1:), &
-      [self % output_size] &
-    )
+    associate(n => self % input_size * self % output_size)
+      ! reshape the weights
+      p_(1:self % input_size, 1:self % output_size) => params(1 : n)
+      self % weights = p_
+
+      ! reshape the biases
+      self % biases = params(n + 1 : n + self % output_size)
+    end associate
 
   end subroutine set_params
 
diff --git a/src/nf/nf_layer.f90 b/src/nf/nf_layer.f90
index e9e90da8..ca5e9606 100644
--- a/src/nf/nf_layer.f90
+++ b/src/nf/nf_layer.f90
@@ -129,7 +129,7 @@ elemental module function get_num_params(self) result(num_params)
         !! Number of parameters in this layer
     end function get_num_params
 
-    pure module function get_params(self) result(params)
+    module function get_params(self) result(params)
       !! Returns the parameters of this layer.
       class(layer), intent(in) :: self
         !! Layer instance
@@ -137,7 +137,7 @@ pure module function get_params(self) result(params)
         !! Parameters of this layer
     end function get_params
 
-    pure module function get_gradients(self) result(gradients)
+    module function get_gradients(self) result(gradients)
       !! Returns the gradients of this layer.
       class(layer), intent(in) :: self
         !! Layer instance
diff --git a/src/nf/nf_layer_submodule.f90 b/src/nf/nf_layer_submodule.f90
index 07467643..c672581a 100644
--- a/src/nf/nf_layer_submodule.f90
+++ b/src/nf/nf_layer_submodule.f90
@@ -298,7 +298,7 @@ elemental module function get_num_params(self) result(num_params)
 
   end function get_num_params
 
-  pure module function get_params(self) result(params)
+  module function get_params(self) result(params)
     class(layer), intent(in) :: self
     real, allocatable :: params(:)
 
@@ -323,7 +323,7 @@ pure module function get_params(self) result(params)
 
   end function get_params
 
-  pure module function get_gradients(self) result(gradients)
+  module function get_gradients(self) result(gradients)
     class(layer), intent(in) :: self
     real, allocatable :: gradients(:)
 
diff --git a/src/nf/nf_loss.f90 b/src/nf/nf_loss.f90
index ceeb4390..75e47f92 100644
--- a/src/nf/nf_loss.f90
+++ b/src/nf/nf_loss.f90
@@ -7,6 +7,7 @@ module nf_loss
   !! loss type that extends the abstract loss derived type, and that
   !! implements concrete eval and derivative methods that accept vectors.
 
+  use nf_metrics, only: metric_type
   implicit none
 
   private
@@ -14,18 +15,12 @@ module nf_loss
   public :: mse
   public :: quadratic
 
-  type, abstract :: loss_type
+  type, extends(metric_type), abstract :: loss_type
   contains
-    procedure(loss_interface), nopass, deferred :: eval
     procedure(loss_derivative_interface), nopass, deferred :: derivative
   end type loss_type
 
   abstract interface
-    pure function loss_interface(true, predicted) result(res)
-      real, intent(in) :: true(:)
-      real, intent(in) :: predicted(:)
-      real :: res
-    end function loss_interface
     pure function loss_derivative_interface(true, predicted) result(res)
       real, intent(in) :: true(:)
       real, intent(in) :: predicted(:)
diff --git a/src/nf/nf_metrics.f90 b/src/nf/nf_metrics.f90
new file mode 100644
index 00000000..43bc12d7
--- /dev/null
+++ b/src/nf/nf_metrics.f90
@@ -0,0 +1,72 @@
+module nf_metrics
+
+  !! This module provides a collection of metric functions.
+
+  implicit none
+
+  private
+  public :: metric_type
+  public :: corr
+  public :: maxabs
+
+  type, abstract :: metric_type
+  contains
+    procedure(metric_interface), nopass, deferred :: eval
+  end type metric_type
+
+  abstract interface
+    pure function metric_interface(true, predicted) result(res)
+      real, intent(in) :: true(:)
+      real, intent(in) :: predicted(:)
+      real :: res
+    end function metric_interface
+  end interface
+
+  type, extends(metric_type) :: corr
+    !! Pearson correlation
+  contains
+    procedure, nopass :: eval => corr_eval
+  end type corr
+
+  type, extends(metric_type) :: maxabs
+    !! Maximum absolute difference
+  contains
+    procedure, nopass :: eval => maxabs_eval
+  end type maxabs
+
+  contains
+
+  pure module function corr_eval(true, predicted) result(res)
+    !! Pearson correlation function:
+    !!
+    real, intent(in) :: true(:)
+      !! True values, i.e. labels from training datasets
+    real, intent(in) :: predicted(:)
+      !! Values predicted by the network
+    real :: res
+      !! Resulting correlation value
+    real :: m_true, m_pred
+
+    m_true = sum(true) / size(true)
+    m_pred = sum(predicted) / size(predicted)
+
+    res = dot_product(true - m_true, predicted - m_pred) / &
+      sqrt(sum((true - m_true)**2)*sum((predicted - m_pred)**2))
+
+  end function corr_eval
+
+  pure function maxabs_eval(true, predicted) result(res)
+    !! Maximum absolute difference function:
+    !!
+    real, intent(in) :: true(:)
+      !! True values, i.e. labels from training datasets
+    real, intent(in) :: predicted(:)
+      !! Values predicted by the network
+    real :: res
+      !! Resulting maximum absolute difference value
+
+    res = maxval(abs(true - predicted))
+
+  end function maxabs_eval
+
+end module nf_metrics
diff --git a/src/nf/nf_network.f90 b/src/nf/nf_network.f90
index 4346f4e3..bcf10ae8 100644
--- a/src/nf/nf_network.f90
+++ b/src/nf/nf_network.f90
@@ -3,6 +3,7 @@ module nf_network
   !! This module provides the network type to create new models.
 
   use nf_layer, only: layer
+  use nf_metrics, only: metric_type
   use nf_loss, only: loss_type
   use nf_optimizers, only: optimizer_base_type
 
@@ -28,6 +29,7 @@ module nf_network
     procedure :: train
     procedure :: update
 
+    procedure, private :: evaluate_batch_1d
     procedure, private :: forward_1d
     procedure, private :: forward_3d
     procedure, private :: predict_1d
@@ -35,6 +37,7 @@ module nf_network
     procedure, private :: predict_batch_1d
     procedure, private :: predict_batch_3d
 
+    generic :: evaluate => evaluate_batch_1d
     generic :: forward => forward_1d, forward_3d
     generic :: predict => predict_1d, predict_3d, predict_batch_1d, predict_batch_3d
 
@@ -62,6 +65,16 @@ end function network_from_keras
 
   end interface network
 
+  interface evaluate
+    module function evaluate_batch_1d(self, input_data, output_data, metric) result(res)
+      class(network), intent(in out) :: self
+      real, intent(in) :: input_data(:,:)
+      real, intent(in) :: output_data(:,:)
+      class(metric_type), intent(in), optional :: metric
+      real, allocatable :: res(:,:)
+    end function evaluate_batch_1d
+  end interface evaluate
+
   interface forward
 
     pure module subroutine forward_1d(self, input)
@@ -159,7 +172,7 @@ pure module integer function get_num_params(self)
       !! Network instance
     end function get_num_params
 
-    pure module function get_params(self) result(params)
+    module function get_params(self) result(params)
       !! Get the network parameters (weights and biases).
       class(network), intent(in) :: self
         !! Network instance
@@ -167,7 +180,7 @@ pure module function get_params(self) result(params)
         !! Network parameters to get
     end function get_params
 
-    pure module function get_gradients(self) result(gradients)
+    module function get_gradients(self) result(gradients)
       class(network), intent(in) :: self
         !! Network instance
       real, allocatable :: gradients(:)
diff --git a/src/nf/nf_network_submodule.f90 b/src/nf/nf_network_submodule.f90
index 59abcf7d..60fa8579 100644
--- a/src/nf/nf_network_submodule.f90
+++ b/src/nf/nf_network_submodule.f90
@@ -337,6 +337,36 @@ pure module subroutine backward(self, output, loss)
   end subroutine backward
 
 
+  module function evaluate_batch_1d(self, input_data, output_data, metric) result(res)
+    class(network), intent(in out) :: self
+    real, intent(in) :: input_data(:,:)
+    real, intent(in) :: output_data(:,:)
+    class(metric_type), intent(in), optional :: metric
+    real, allocatable :: res(:,:)
+
+    integer :: i, n
+    real, allocatable :: output(:,:)
+
+    output = self % predict(input_data)
+
+    n = 1
+    if (present(metric)) n = n + 1
+
+    allocate(res(size(output, dim=1), n))
+
+    do concurrent (i = 1:size(output, dim=1))
+      res(i,1) = self % loss % eval(output_data(i,:), output(i,:))
+    end do
+
+    if (.not. present(metric)) return
+
+    do concurrent (i = 1:size(output, dim=1))
+      res(i,2) = metric % eval(output_data(i,:), output(i,:))
+    end do
+
+  end function evaluate_batch_1d
+
+
   pure module subroutine forward_1d(self, input)
     class(network), intent(in out) :: self
     real, intent(in) :: input(:)
@@ -496,7 +526,7 @@ pure module function get_num_params(self)
   end function get_num_params
 
 
-  pure module function get_params(self) result(params)
+  module function get_params(self) result(params)
     class(network), intent(in) :: self
     real, allocatable :: params(:)
     integer :: n, nstart, nend
@@ -516,7 +546,7 @@ pure module function get_params(self) result(params)
   end function get_params
 
 
-  pure module function get_gradients(self) result(gradients)
+  module function get_gradients(self) result(gradients)
     class(network), intent(in) :: self
     real, allocatable :: gradients(:)
     integer :: n, nstart, nend
@@ -566,11 +596,10 @@ module subroutine train(self, input_data, output_data, batch_size, &
     integer, intent(in) :: epochs
     class(optimizer_base_type), intent(in), optional :: optimizer
     class(loss_type), intent(in), optional :: loss
-    class(optimizer_base_type), allocatable :: optimizer_
 
     real :: pos
     integer :: dataset_size
-    integer :: batch_start, batch_end
+    integer :: batch_start
     integer :: i, j, n
     integer :: istart, iend, indices(2)
 
@@ -600,11 +629,9 @@ module subroutine train(self, input_data, output_data, batch_size, &
         ! Pull a random mini-batch from the dataset
         call random_number(pos)
         batch_start = int(pos * (dataset_size - batch_size + 1)) + 1
-        batch_end = batch_start + batch_size - 1
 
         ! FIXME shuffle in a way that doesn't require co_broadcast
         call co_broadcast(batch_start, 1)
-        call co_broadcast(batch_end, 1)
 
         ! Distribute the batch in nearly equal pieces to all images
         indices = tile_indices(batch_size)
@@ -628,7 +655,6 @@ module subroutine update(self, optimizer, batch_size)
     class(network), intent(in out) :: self
     class(optimizer_base_type), intent(in), optional :: optimizer
     integer, intent(in), optional :: batch_size
-    class(optimizer_base_type), allocatable :: optimizer_
     integer :: batch_size_
     real, allocatable :: params(:)
     integer :: n
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 6903deb7..1be8bb8d 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -17,6 +17,7 @@ foreach(execid
   conv2d_network
   optimizers
   loss
+  metrics
   )
   add_executable(test_${execid} test_${execid}.f90)
   target_link_libraries(test_${execid} PRIVATE neural-fortran h5fortran::h5fortran jsonfortran::jsonfortran ${LIBS})
diff --git a/test/test_flatten_layer.f90 b/test/test_flatten_layer.f90
index cc780acd..70641c9c 100644
--- a/test/test_flatten_layer.f90
+++ b/test/test_flatten_layer.f90
@@ -9,7 +9,7 @@ program test_flatten_layer
 
   type(layer) :: test_layer, input_layer
   type(network) :: net
-  real, allocatable :: input_data(:,:,:), gradient(:,:,:)
+  real, allocatable :: gradient(:,:,:)
   real, allocatable :: output(:)
   logical :: ok = .true.
 
diff --git a/test/test_get_set_network_params.f90 b/test/test_get_set_network_params.f90
index b9cc24b1..3e285f3c 100644
--- a/test/test_get_set_network_params.f90
+++ b/test/test_get_set_network_params.f90
@@ -3,7 +3,6 @@ program test_get_set_network_params
   use nf, only: conv2d, dense, flatten, input, maxpool2d, network
   implicit none
   type(network) :: net
-  integer :: n
   logical :: ok = .true.
   real :: test_params_dense(8) = [1, 2, 3, 4, 5, 6, 7, 8]
   real :: test_params_conv2d(10) = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
diff --git a/test/test_metrics.f90 b/test/test_metrics.f90
new file mode 100644
index 00000000..a9db74ed
--- /dev/null
+++ b/test/test_metrics.f90
@@ -0,0 +1,70 @@
+program test_metrics
+  use iso_fortran_env, only: stderr => error_unit
+  use nf, only: dense, input, network, sgd, mse
+  implicit none
+  type(network) :: net
+  logical :: ok = .true.
+
+  ! Minimal 2-layer network
+  net = network([ &
+    input(1), &
+    dense(1) &
+  ])
+
+  training: block
+    real :: x(1), y(1)
+    real :: tolerance = 1e-3
+    integer :: n
+    integer, parameter :: num_iterations = 1000 
+    real :: quadratic_loss, mse_metric
+    real, allocatable :: metrics(:,:)
+
+    x = [0.1234567]
+    y = [0.7654321]
+
+    do n = 1, num_iterations
+      call net % forward(x)
+      call net % backward(y)
+      call net % update(sgd(learning_rate=1.))
+      if (all(abs(net % predict(x) - y) < tolerance)) exit
+    end do
+    
+    ! Returns only one metric, based on the default loss function (quadratic).
+    metrics = net % evaluate(reshape(x, [1, 1]), reshape(y, [1, 1]))
+    quadratic_loss = metrics(1,1)
+
+    if (.not. all(shape(metrics) == [1, 1])) then
+      write(stderr, '(a)') 'metrics array is the correct shape (1, 1).. failed'
+      ok = .false.
+    end if
+
+    ! Returns two metrics, one from the loss function and another specified by the user.
+    metrics = net % evaluate(reshape(x, [1, 1]), reshape(y, [1, 1]), metric=mse())
+
+    if (.not. all(shape(metrics) == [1, 2])) then
+      write(stderr, '(a)') 'metrics array is the correct shape (1, 2).. failed'
+      ok = .false.
+    end if
+
+    mse_metric = metrics(1,2)
+
+    if (.not. all(metrics < 1e-5)) then
+      write(stderr, '(a)') 'value for all metrics is expected.. failed'
+      ok = .false.
+    end if
+
+    if (.not. metrics(1,1) == quadratic_loss) then
+      write(stderr, '(a)') 'first metric should be the same as that of the loss function.. failed'
+      ok = .false.
+    end if
+
+  end block training
+
+  if (ok) then
+    print '(a)', 'test_metrics: All tests passed.'
+  else
+    write(stderr, '(a)') 'test_metrics: One or more tests failed.'
+    stop 1
+  end if
+
+end program test_metrics
diff --git a/test/test_optimizers.f90 b/test/test_optimizers.f90
index dc2cc03a..4ed1d927 100644
--- a/test/test_optimizers.f90
+++ b/test/test_optimizers.f90
@@ -8,7 +8,7 @@ program test_optimizers
   real, allocatable :: x(:), y(:)
   real, allocatable :: ypred(:)
   integer, parameter :: num_iterations = 1000
-  integer :: n, i
+  integer :: n
   logical :: ok = .true.
   logical :: converged = .false.