Ch15 figs (rasbt#41)

* Added ch15 images/ folder * Added images to part1 * Added images to part2 * Run all cells (part1) * Run all cells (part2) * Removed extra files
voloferenc · Nov 4, 2019 · 224fc6e · 224fc6e
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commit 224fc6e
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Showing 24 changed files with 570 additions and 133 deletions.
diff --git a/ch15/ch15_part1.ipynb b/ch15/ch15_part1.ipynb
diff --git a/ch15/ch15_part1.py b/ch15/ch15_part1.py
@@ -25,16 +25,42 @@
 
 
 
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 # ##  The building blocks of convolutional neural networks
 # 
 # ### Understanding CNNs and feature hierarchies
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+# 
 # 
 # ### Performing discrete convolutions
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 # ### Discrete convolutions in one dimension
 # 
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 # ### Padding inputs to control the size of the output feature maps
 # 
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 # ### Determining the size of the convolution output
 
 
@@ -77,6 +103,18 @@ def conv1d(x, w, p=0, s=1):
 
 
 
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 def conv2d(X, W, p=(0, 0), s=(1, 1)):
     W_rot = np.array(W)[::-1,::-1]
     X_orig = np.array(X)
@@ -110,12 +148,20 @@ def conv2d(X, W, p=(0, 0), s=(1, 1)):
 
 # ## Subsampling layers
 
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 # ## Putting everything together – implementing a CNN
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 # ### Working with multiple input or color channels
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 # 
 
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 # **TIP: Reading an image file**
 
 
@@ -169,6 +215,10 @@ def conv2d(X, W, p=(0, 0), s=(1, 1)):
 
 
 
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 conv_layer = keras.layers.Conv2D(
     filters=16, kernel_size=(3, 3),
     kernel_regularizer=keras.regularizers.l2(0.001))
@@ -194,6 +244,10 @@ def conv2d(X, W, p=(0, 0), s=(1, 1)):
 
 
 
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 ####### Binary Crossentropy
 bce_probas = tf.keras.losses.BinaryCrossentropy(from_logits=False)
 bce_logits = tf.keras.losses.BinaryCrossentropy(from_logits=True)
@@ -240,6 +294,10 @@ def conv2d(X, W, p=(0, 0), s=(1, 1)):
 # 
 # ### The multilayer CNN architecture
 
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 # ### Loading and preprocessing the data
 
 

diff --git a/ch15/ch15_part2.ipynb b/ch15/ch15_part2.ipynb
diff --git a/ch15/ch15_part2.py b/ch15/ch15_part2.py
@@ -27,6 +27,7 @@
 
 
 
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 # ## Gender classification from face images using CNN
 # 
 
@@ -120,7 +121,7 @@ def count_items(ds):
     img_center_crop, size=(218, 178))
 ax.imshow(img_resized.numpy().astype('uint8'))
 
-#plt.savefig('figures/15_14.png', dpi=300)
+# plt.savefig('figures/15_14.png', dpi=300)
 plt.show()
 
 
@@ -156,7 +157,7 @@ def count_items(ds):
     if i == 0:
         ax.set_title('Step 3: Resize', size=15)
 
-#plt.savefig('figures/15_15.png', dpi=300)
+# plt.savefig('figures/15_15.png', dpi=300)
 plt.show()
 
 
@@ -226,6 +227,12 @@ def preprocess(example, size=(64, 64), mode='train'):
 
 
 # ### Training a CNN gender classifier
+# 
+# * **Global Average Pooling**
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diff --git a/ch15/figures/15_12.png b/ch15/figures/15_12.png
diff --git a/ch15/figures/15_13.png b/ch15/figures/15_13.png
diff --git a/ch15/figures/15_14.png b/ch15/figures/15_14.png
diff --git a/ch15/figures/15_15.png b/ch15/figures/15_15.png
diff --git a/ch15/figures/15_16.png b/ch15/figures/15_16.png
diff --git a/ch15/figures/15_18.png b/ch15/figures/15_18.png
diff --git a/ch15/figures/figures-15_19.png b/ch15/figures/figures-15_19.png
diff --git a/ch15/images/15_01.png b/ch15/images/15_01.png
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diff --git a/ch15/images/15_12.png b/ch15/images/15_12.png
diff --git a/ch15/images/15_13.png b/ch15/images/15_13.png