feat: mnist test

autoencoder.py

@@ -71,6 +71,6 @@ class Autoencoder:
 
     def encode(self, v: np.ndarray) -> np.ndarray:
         return self.encoder.forward(v)
-    
+
     def decode(self, v: np.ndarray) -> np.ndarray:
         return self.decoder.forward(v)

mnist_test.py

@@ -0,0 +1,51 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from autoencoder import Autoencoder
+from utils import (relu,
+                   dynamic_loss_plot_init,
+                   dynamic_loss_plot_update,
+                   dynamic_loss_plot_finish)
+
+
+def mnist_embed():
+    import keras
+    (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+    IN_LEN = x_train[0].flatten().shape[0]
+    DIM = 50
+    autoencoder = Autoencoder(IN_LEN, DIM, 0.001, relu)
+    ax, line = dynamic_loss_plot_init()
+    NO_IMPROV = 0
+    prev_error = float('inf')
+    losses = []
+    epoch = 0
+    x_train = x_train[:1_000]
+    while True:
+        error = 0
+        for x in x_train:
+            input = x.flatten() / 255
+            error += autoencoder.train(input)
+        error /= len(x_train)
+        if error >= prev_error:
+            NO_IMPROV += 1
+        prev_error = error
+        losses.append(error)
+        dynamic_loss_plot_update(ax, line, losses)
+        if NO_IMPROV > 10:
+            print('Done !')
+            break
+        if epoch > 200:
+            break
+        epoch += 1
+    dynamic_loss_plot_finish(ax, line)
+    example: np.ndarray = x_test[np.random.randint(0, len(x_test))]
+    code = autoencoder.encode(example.flatten() / 255)
+    output = autoencoder.decode(code)
+    plt.subplot(1, 2, 1)
+    plt.matshow(example, fignum=False)
+    plt.subplot(1, 2, 2)
+    plt.matshow(output.reshape(example.shape), fignum=False)
+    plt.show()
+
+
+if __name__ == "__main__":
+    mnist_embed()