Fix bugs on Tensorflow tutorials

03 Writing Algorithms/31 Machine Learning/03 Popular Libraries/08 Tensorflow/04 Build Models.html

@@ -20,15 +20,15 @@
 </table>
 
 <p>The following image shows the time difference between the features and labels:</p>
-<img class="docs-image" alt="Features and labels for training" src="https://cdn.quantconnect.com/i/tu/ml-keras-function.png">
+<img class="docs-image" alt="Features and labels for training" src="https://cdn.quantconnect.com/i/tu/ml-tf.png">
 
 <p>Follow these steps to create a method to build the model:</p>
 <ol>
     <li>Define some parameters, including the number of factors, neurons, and epochs.</li>
     <div class="section-example-container">
         <pre class="python">num_factors = 5
 num_neurons_1 = 10
-num_neurons_2 = 20
+num_neurons_2 = 10
 num_neurons_3 = 5
 self.epochs = 20
 self.learning_rate = 0.0001</pre>

03 Writing Algorithms/31 Machine Learning/03 Popular Libraries/08 Tensorflow/05 Train Models.html

@@ -3,7 +3,7 @@
 <h4>Warm Up Training Data</h4>
 <p>You need historical data to initially train the model at the start of your algorithm. To get the initial training data, in the <code class="csharp">Initialize</code><code class="python">initialize</code> method, make a <a href="/docs/v2/writing-algorithms/historical-data/history-requests">history request</a>.</p>
 <div class="section-example-container">
-    <pre class="python">training_length = 252*2
+    <pre class="python">training_length = 300
 self.training_data = RollingWindow[float](training_length)
 history = self.history[TradeBar](self._symbol, training_length, Resolution.DAILY)
 for trade_bar in history:

03 Writing Algorithms/31 Machine Learning/03 Popular Libraries/08 Tensorflow/06 Predict Labels.html

@@ -9,6 +9,6 @@
 <div class="section-example-container">
     <pre class="python">if prediction > 0:
     self.SetHoldings(self._symbol, 1)
-else:            
+elif prediction < 0:
     self.SetHoldings(self._symbol, -1)</pre>
 </div>

03 Writing Algorithms/31 Machine Learning/03 Popular Libraries/08 Tensorflow/09 Clone Example Algorithm.html

@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_01de7b4c0cf9740cbbd43a4542a882a2.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_036ce75f20b3e7162f5179fd08947bcb.html"></iframe>
     </div>
 </div>

04 Research Environment/08 Machine Learning/03 TensorFlow/04 Prepare Data.html

@@ -25,7 +25,9 @@
     <li>Loop through the DataFrame of historical prices and collect the features.</li>
 
     <div class="section-example-container">
-        <pre class="python">lookback = 5
+        <pre class="python">data = history
+
+lookback = 5
 lookback_series = []
 for i in range(1, lookback + 1):
     df = data['close'].diff(i)[lookback:-1]
@@ -36,7 +38,7 @@
     </div>
 
     <p>The following image shows the format of the features DataFrame:</p>
-    <img class="docs-image" src="https://cdn.quantconnect.com/i/tu/tf-x.png" alt="Features and labels for training">
+    <img class="docs-image" src="https://cdn.quantconnect.com/i/tu/ml-tf.png" alt="Features and labels for training">
 
     <li>Select the close column and then call the <code>shift</code> method to collect the labels.</li>
     <div class="section-example-container">

04 Research Environment/08 Machine Learning/03 TensorFlow/05 Train Models.html

@@ -7,7 +7,7 @@ <h4>Build the Model</h4>
     <div class="section-example-container">
         <pre class="python">num_factors = X_test.shape[1]
 num_neurons_1 = 10
-num_neurons_2 = 20
+num_neurons_2 = 10
 num_neurons_3 = 5
 epochs = 20
 learning_rate = 0.0001</pre>