Update for chapter 3 and TF v1.7

nfmcclure · Apr 1, 2018 · 7dd2714 · 7dd2714
1 parent 0234ac9
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diff --git a/03_Linear_Regression/05_Implementing_Deming_Regression/05_deming_regression.py b/03_Linear_Regression/05_Implementing_Deming_Regression/05_deming_regression.py
@@ -16,6 +16,10 @@
 from tensorflow.python.framework import ops
 ops.reset_default_graph()
 
+# Set a random seed
+tf.set_random_seed(42)
+np.random.seed(42)
+
 # Create graph
 sess = tf.Session()
 
@@ -45,7 +49,7 @@
 loss = tf.reduce_mean(tf.truediv(demming_numerator, demming_denominator))
 
 # Declare optimizer
-my_opt = tf.train.GradientDescentOptimizer(0.1)
+my_opt = tf.train.GradientDescentOptimizer(0.15)
 train_step = my_opt.minimize(loss)
 
 # Initialize variables
@@ -54,14 +58,14 @@
 
 # Training loop
 loss_vec = []
-for i in range(250):
+for i in range(1000):
     rand_index = np.random.choice(len(x_vals), size=batch_size)
     rand_x = np.transpose([x_vals[rand_index]])
     rand_y = np.transpose([y_vals[rand_index]])
     sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})
     temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
     loss_vec.append(temp_loss)
-    if (i+1)%50==0:
+    if (i+1)%50 == 0:
         print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b)))
         print('Loss = ' + str(temp_loss))
 

diff --git a/03_Linear_Regression/05_Implementing_Deming_Regression/05_demming_regression.ipynb b/03_Linear_Regression/05_Implementing_Deming_Regression/05_demming_regression.ipynb
@@ -65,7 +65,11 @@
    },
    "outputs": [],
    "source": [
-    "sess = tf.Session()"
+    "sess = tf.Session()\n",
+    "\n",
+    "# Set a random seed\n",
+    "tf.set_random_seed(42)\n",
+    "np.random.seed(42)"
    ]
   },
   {
@@ -365,7 +369,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/...ar_Regression/06_Implementing_Lasso_and_Ridge_Regression/06_lasso_and_ridge_regression.py b/...ar_Regression/06_Implementing_Lasso_and_Ridge_Regression/06_lasso_and_ridge_regression.py
@@ -18,7 +18,8 @@
 
 
 # Specify 'Ridge' or 'LASSO'
-regression_type = 'LASSO'
+#regression_type = 'LASSO'
+regression_type = 'Ridge'
 
 # clear out old graph
 ops.reset_default_graph()

diff --git a/03_Linear_Regression/08_Implementing_Logistic_Regression/08_logistic_regression.ipynb b/03_Linear_Regression/08_Implementing_Logistic_Regression/08_logistic_regression.ipynb
@@ -74,7 +74,7 @@
     "    birth_data = birth_file.text.split('\\r\\n')\n",
     "    birth_header = birth_data[0].split('\\t')\n",
     "    birth_data = [[float(x) for x in y.split('\\t') if len(x)>=1] for y in birth_data[1:] if len(y)>=1]\n",
-    "    with open(birth_weight_file, \"w\") as f:\n",
+    "    with open(birth_weight_file, 'w', newline='') as f:\n",
     "        writer = csv.writer(f)\n",
     "        writer.writerow(birth_header)\n",
     "        writer.writerows(birth_data)\n",

diff --git a/03_Linear_Regression/08_Implementing_Logistic_Regression/08_logistic_regression.py b/03_Linear_Regression/08_Implementing_Logistic_Regression/08_logistic_regression.py
@@ -27,23 +27,23 @@
 # Obtain and prepare data for modeling
 ###
 
-# name of data file
+# Set name of data file
 birth_weight_file = 'birth_weight.csv'
 
-# download data and create data file if file does not exist in current directory
+# Download data and create data file if file does not exist in current directory
 if not os.path.exists(birth_weight_file):
     birthdata_url = 'https://github.com/nfmcclure/tensorflow_cookbook/raw/master/01_Introduction/07_Working_with_Data_Sources/birthweight_data/birthweight.dat'
     birth_file = requests.get(birthdata_url)
     birth_data = birth_file.text.split('\r\n')
     birth_header = birth_data[0].split('\t')
     birth_data = [[float(x) for x in y.split('\t') if len(x)>=1] for y in birth_data[1:] if len(y)>=1]
-    with open(birth_weight_file, "w") as f:
+    with open(birth_weight_file, 'w', newline='') as f:
         writer = csv.writer(f)
         writer.writerow(birth_header)
         writer.writerows(birth_data)
         f.close()
 
-# read birth weight data into memory
+# Read birth weight data into memory
 birth_data = []
 with open(birth_weight_file, newline='') as csvfile:
      csv_reader = csv.reader(csvfile)
@@ -58,7 +58,7 @@
 # Pull out predictor variables (not id, not target, and not birthweight)
 x_vals = np.array([x[1:8] for x in birth_data])
 
-# set for reproducible results
+# Set for reproducible results
 seed = 99
 np.random.seed(seed)
 tf.set_random_seed(seed)

diff --git a/requirements.txt b/requirements.txt
@@ -1,4 +1,4 @@
-tensorflow==1.6.0
+tensorflow==1.7.0
 numpy==1.14.2
 scipy==1.0.0
 sklearn==0.19.1