Run all cells (part1)

david2194 · Nov 4, 2019 · c9a739a · c9a739a
1 parent f8c920f
commit c9a739a
Showing 2 changed files with 294 additions and 1,550 deletions.
diff --git a/ch16/ch16_part1.ipynb b/ch16/ch16_part1.ipynb
diff --git a/ch16/ch16_part1.py b/ch16/ch16_part1.py
@@ -31,22 +31,57 @@
 
 
 
+
+
+
+
 # # Introducing sequential data
 # 
 # ## Modeling sequential data⁠—order matters
 # 
 # ## Representing sequences
 # 
+# 
+
+
+
+
+
 # ## The different categories of sequence modeling
 
+
+
+
+
 # # RNNs for modeling sequences
 # 
 # ## Understanding the RNN looping mechanism
 # 
+
+
+
+
+
+
+
+
+
 # ## Computing activations in an RNN
 # 
+
+
+
+
+
+
+
+
+
 # ## Hidden-recurrence vs. output-recurrence
-# 
+
+
+
+
 
 
 
@@ -98,9 +133,19 @@
 
 
 # ## The challenges of learning long-range interactions
+# 
+
+
+
+
+
 # 
 # ## Long Short-Term Memory cells 
 
+
+
+
+
 # # Implementing RNNs for sequence modeling in TensorFlow
 # 
 # ## Project one: predicting the sentiment of IMDb movie reviews
@@ -261,7 +306,7 @@ def encode_map_fn(text, label):
     32, padded_shapes=([-1],[]))
 
 
-# #### Embedding layers for sentence encoding
+# ### Embedding layers for sentence encoding
 # 
 # 
 #  * `input_dim`: number of words, i.e. maximum integer index + 1.
@@ -281,6 +326,10 @@ def encode_map_fn(text, label):
 
 
 
+
+
+
+
 model = tf.keras.Sequential()
 
 model.add(Embedding(input_dim=100,