Print all netout and moved th downloads of all files in seperate function

lenet5.py

@@ -31,6 +31,23 @@ def download_file(url, file_name):
         response = get(url)
         file.write(response.content)
 
+def download_files():
+    """
+    Download all data and label files.
+    """
+    # train-images-idx3-ubyte.gz: training set images (9912422 bytes)
+    download_file('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
+                  'train-images-idx3-ubyte.gz')
+    # train-labels-idx1-ubyte.gz: training set labels (28881 bytes)
+    download_file('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
+                  'train-labels-idx1-ubyte.gz')
+    # t10k-images-idx3-ubyte.gz: test set images (1648877 bytes)
+    download_file('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
+                  't10k-images-idx3-ubyte.gz')
+    # t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)
+    download_file('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',
+                  't10k-labels-idx1-ubyte.gz')
+
 def read_mnist(images_path: str, labels_path: str):
     """
     Read data and labels of the MNIST dataset.
@@ -128,19 +145,7 @@ def main():
 
     # Step 1:
     # Download the MNIST dataset with consist of labeled handwritten images (28x28 px).
-
-    # train-images-idx3-ubyte.gz: training set images (9912422 bytes)
-    download_file('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
-                  'train-images-idx3-ubyte.gz')
-    # train-labels-idx1-ubyte.gz: training set labels (28881 bytes)
-    download_file('http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
-                  'train-labels-idx1-ubyte.gz')
-    # t10k-images-idx3-ubyte.gz: test set images (1648877 bytes)
-    download_file('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
-                  't10k-images-idx3-ubyte.gz')
-    # t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)
-    download_file('http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',
-                  't10k-labels-idx1-ubyte.gz')
+    download_files()
 
     # Step 2:
     # Read MNIST dataset (training and testing)
@@ -190,10 +195,9 @@ def main():
     model = create_lenet5()
 
     # Step 8:
-    # Train the LeNet-5 model
-    train_lenet5(model)
-
-    model.save('lenet5.h5')
+    # Train and save the LeNet-5 model
+    # train_lenet5(model)
+    # model.save('lenet5.h5')
 
     model = load_model('lenet5.h5')
 
@@ -210,12 +214,12 @@ def main():
     # display_image(test, 1)
     # display_image(test, 2)
 
-    outputs = model.predict(test['features'])
+    netouts = model.predict(test['features'])
 
-    for i, item in enumerate(outputs[0]):
-        print("%d: %.3f" % (i, item))
-
-    print(test['labels'][0])
+    for i, netout in enumerate(netouts):
+        print("%d: %.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f"
+              % (i, netout[0], netout[1], netout[2], netout[3], netout[4],
+                    netout[5], netout[6], netout[7], netout[8], netout[9]))
 
 if __name__ == "__main__":
     main()