pt1 dli fix

Author: calderjo

learntools/deep_learning_intro/dltools.py

@@ -1,6 +1,6 @@
-from tensorflow import keras
+from tf_keras
 import tensorflow as tf
-from tensorflow.keras import layers
+from tf_keras import layers
 
 import numpy as np
 

learntools/deep_learning_intro/ex1.py

@@ -1,5 +1,6 @@
 import numpy as np
 import tensorflow as tf
+import tf_keras
 from learntools.core import *
 
 # Using *Red Wine Quality* dataset
@@ -29,16 +30,16 @@ def check(self, input_shape):
 class Q2(CodingProblem):
     _hint = """Your answer should look something like:
 ```python
-model = keras.Sequential([
+model = tf_keras.Sequential([
     ____
 ])
 ```
 """
     _solution = CS("""
-from tensorflow import keras
-from tensorflow.keras import layers
+from tf_keras
+from tf_keras import layers
 
-model = keras.Sequential([
+model = tf_keras.Sequential([
     layers.Dense(units=1, input_shape=[{}])
 ])
 """.format(inputs))
@@ -66,7 +67,7 @@ class Q3(CodingProblem):
     _hint = "You can get the attribute of an object using the 'dot' notation: like `object.attribute`."
     _solution = CS(r"""
 # Uncomment if you need the model from the previous question:
-# model = keras.Sequential([
+# model = tf_keras.Sequential([
 #     layers.Dense(units=1, input_shape=[11])
 # ])
 

learntools/deep_learning_intro/ex2.py

@@ -1,6 +1,6 @@
 import numpy as np
 import tensorflow as tf
-
+import tf_keras
 from learntools.core import *
 
 # Using *Concrete* dataset
@@ -33,16 +33,16 @@ def check(self, input_shape):
 class Q2(CodingProblem):
     _hint = """Your answer should look something like:
 ```python
-model = keras.Sequential([
+model = tf_keras.Sequential([
     ____
 ])
 ```
 """
     _solution = CS("""
-from tensorflow import keras
-from tensorflow.keras import layers
+import tf_keras
+from tf_keras import layers
 
-model = keras.Sequential([
+model = tf_keras.Sequential([
     layers.Dense(512, activation='relu', input_shape=input_shape),
     layers.Dense(512, activation='relu'),
     layers.Dense(512, activation='relu'),    
@@ -85,7 +85,7 @@ class Q3(CodingProblem):
     _hidden_units = 32
     _hint = """Your model should look something like:
 ```python
-model = keras.Sequential([
+model = tf_keras.Sequential([
     layers.Dense(____),
     layers.Activation(____),
     layers.Dense(____),
@@ -95,7 +95,7 @@ class Q3(CodingProblem):
 ```
 """
     _solution = CS("""
-model = keras.Sequential([
+model = tf_keras.Sequential([
     layers.Dense(32, input_shape=[8]),
     layers.Activation('relu'),
     layers.Dense(32),

learntools/deep_learning_intro/ex5.py

@@ -5,7 +5,7 @@
 class Q1(CodingProblem):
     _hint = """Your answer should look something like:
 ```python
-model = keras.Sequential([
+model = tf_keras.Sequential([
     # Dense
     # Dropout
     # Dense
@@ -15,7 +15,7 @@ class Q1(CodingProblem):
 ```
 """
     _solution = CS("""
-model = keras.Sequential([
+model = tf_keras.Sequential([
     layers.Dense(128, activation='relu', input_shape=input_shape),
     layers.Dropout(0.3),
     layers.Dense(64, activation='relu'),
@@ -65,7 +65,7 @@ class Q2(ThoughtExperiment):
 class Q3(CodingProblem):
     _hint = """Your answer should look something like:
 ```python
-model = keras.Sequential([
+model = tf_keras.Sequential([
     # Batch Normalization
     # Dense
     # Batch Normalization
@@ -78,7 +78,7 @@ class Q3(CodingProblem):
 ```
 """
     _solution = CS("""
-model = keras.Sequential([
+model = tf_keras.Sequential([
     layers.BatchNormalization(input_shape=input_shape),
     layers.Dense(512, activation='relu'),
     layers.BatchNormalization(),

learntools/deep_learning_intro/ex6.py

@@ -20,10 +20,10 @@ class Q1(CodingProblem):
 ```
 """
     _solution = CS("""
-from tensorflow import keras
-from tensorflow.keras import layers
+import tf_keras
+from tf_keras import layers
 
-model = keras.Sequential([
+model = tf_keras.Sequential([
     layers.BatchNormalization(input_shape=input_shape),
     layers.Dense(256, activation='relu'),
     layers.BatchNormalization(),