update

2022-10-28 08:14:43 +02:00
parent e3d02b38fe
commit 1c5b6d5130
7 changed files with 173 additions and 222 deletions
@@ -1186,7 +1186,6 @@ train_images, test_images <span style="color: #666666">=</span> train_images <sp
        <div class="highlight" style="background: #f8f8f8">
  <pre style="line-height: 125%;">class_names <span style="color: #666666">=</span> [<span style="color: #BA2121">&#39;airplane&#39;</span>, <span style="color: #BA2121">&#39;automobile&#39;</span>, <span style="color: #BA2121">&#39;bird&#39;</span>, <span style="color: #BA2121">&#39;cat&#39;</span>, <span style="color: #BA2121">&#39;deer&#39;</span>,
               <span style="color: #BA2121">&#39;dog&#39;</span>, <span style="color: #BA2121">&#39;frog&#39;</span>, <span style="color: #BA2121">&#39;horse&#39;</span>, <span style="color: #BA2121">&#39;ship&#39;</span>, <span style="color: #BA2121">&#39;truck&#39;</span>]
-
 plt<span style="color: #666666">.</span>figure(figsize<span style="color: #666666">=</span>(<span style="color: #666666">10</span>,<span style="color: #666666">10</span>))
 <span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">25</span>):
    plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">5</span>,<span style="color: #666666">5</span>,i<span style="color: #666666">+1</span>)
@@ -1277,8 +1276,7 @@ layer with 10 outputs and a softmax activation.
  <pre style="line-height: 125%;">model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Flatten())
 model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(<span style="color: #666666">64</span>, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>))
 model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(<span style="color: #666666">10</span>))
-Here<span style="color: #BA2121">&#39;s the complete architecture of our model.</span>
-
+<span style="color: #408080; font-style: italic">#Here&#39;s the complete architecture of our model.</span>
 model<span style="color: #666666">.</span>summary()
 </pre>
 </div>
@@ -1295,7 +1293,7 @@ model<span style="color: #666666">.</span>summary()
  </div>
 </div>

-<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.</p>
+<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.</p>

 <!-- !split -->
 <h2 id="compile-and-train-the-model" class="anchor">Compile and train the model </h2>
@@ -1310,7 +1308,6 @@ model<span style="color: #666666">.</span>summary()
  <pre style="line-height: 125%;">model<span style="color: #666666">.</span>compile(optimizer<span style="color: #666666">=</span><span style="color: #BA2121">&#39;adam&#39;</span>,
              loss<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>keras<span style="color: #666666">.</span>losses<span style="color: #666666">.</span>SparseCategoricalCrossentropy(from_logits<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>),
              metrics<span style="color: #666666">=</span>[<span style="color: #BA2121">&#39;accuracy&#39;</span>])
-
 history <span style="color: #666666">=</span> model<span style="color: #666666">.</span>fit(train_images, train_labels, epochs<span style="color: #666666">=10</span>, 
                    validation_data<span style="color: #666666">=</span>(test_images, test_labels))
 </pre>
@@ -1345,9 +1342,7 @@ plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39
 plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;Accuracy&#39;</span>)
 plt<span style="color: #666666">.</span>ylim([<span style="color: #666666">0.5</span>, <span style="color: #666666">1</span>])
 plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;lower right&#39;</span>)
-
 test_loss, test_acc <span style="color: #666666">=</span> model<span style="color: #666666">.</span>evaluate(test_images,  test_labels, verbose<span style="color: #666666">=2</span>)
-
 <span style="color: #008000">print</span>(test_acc)
 </pre>
 </div>
@@ -1416,8 +1411,6 @@ systems such as automatic translation and speech-to-text.
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras</span> <span style="color: #008000; font-weight: bold">import</span> regularizers           
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras.utils</span> <span style="color: #008000; font-weight: bold">import</span> to_categorical 

-
-
 <span style="color: #408080; font-style: italic"># convert into dataset matrix</span>
 <span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">convertToMatrix</span>(data, step):
 X, Y <span style="color: #666666">=</span>[], []
@@ -1457,7 +1450,6 @@ model<span style="color: #666666">.</span>add(Dense(<span style="color: #666666"
 model<span style="color: #666666">.</span>add(Dense(<span style="color: #666666">1</span>))
 model<span style="color: #666666">.</span>compile(loss<span style="color: #666666">=</span><span style="color: #BA2121">&#39;mean_squared_error&#39;</span>, optimizer<span style="color: #666666">=</span><span style="color: #BA2121">&#39;rmsprop&#39;</span>)
 model<span style="color: #666666">.</span>summary()
-
 model<span style="color: #666666">.</span>fit(trainX,trainY, epochs<span style="color: #666666">=100</span>, batch_size<span style="color: #666666">=16</span>, verbose<span style="color: #666666">=2</span>)
 trainPredict <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(trainX)
 testPredict<span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(testX)
@@ -1141,7 +1141,6 @@ train_images, test_images = train_images / <span style="color: #B452CD">255.0</s
        <div class="highlight" style="background: #eeeedd">
  <pre style="font-size: 80%; line-height: 125%;">class_names = [<span style="color: #CD5555">&#39;airplane&#39;</span>, <span style="color: #CD5555">&#39;automobile&#39;</span>, <span style="color: #CD5555">&#39;bird&#39;</span>, <span style="color: #CD5555">&#39;cat&#39;</span>, <span style="color: #CD5555">&#39;deer&#39;</span>,
               <span style="color: #CD5555">&#39;dog&#39;</span>, <span style="color: #CD5555">&#39;frog&#39;</span>, <span style="color: #CD5555">&#39;horse&#39;</span>, <span style="color: #CD5555">&#39;ship&#39;</span>, <span style="color: #CD5555">&#39;truck&#39;</span>]
-<span style="color: #a61717; background-color: #e3d2d2"></span>
 plt.figure(figsize=(<span style="color: #B452CD">10</span>,<span style="color: #B452CD">10</span>))
 <span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #B452CD">25</span>):
    plt.subplot(<span style="color: #B452CD">5</span>,<span style="color: #B452CD">5</span>,i+<span style="color: #B452CD">1</span>)
@@ -1233,8 +1232,7 @@ layer with 10 outputs and a softmax activation.
  <pre style="font-size: 80%; line-height: 125%;">model.add(layers.Flatten())
 model.add(layers.Dense(<span style="color: #B452CD">64</span>, activation=<span style="color: #CD5555">&#39;relu&#39;</span>))
 model.add(layers.Dense(<span style="color: #B452CD">10</span>))
-Here<span style="color: #CD5555">&#39;s the complete architecture of our model.</span>
-
+<span style="color: #228B22">#Here&#39;s the complete architecture of our model.</span>
 model.summary()
 </pre>
 </div>
@@ -1251,7 +1249,7 @@ model.summary()
  </div>
 </div>

-<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.</p>
+<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.</p>
 </section>

 <section>
@@ -1267,7 +1265,6 @@ model.summary()
  <pre style="font-size: 80%; line-height: 125%;">model.compile(optimizer=<span style="color: #CD5555">&#39;adam&#39;</span>,
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=<span style="color: #8B008B; font-weight: bold">True</span>),
              metrics=[<span style="color: #CD5555">&#39;accuracy&#39;</span>])
-<span style="color: #a61717; background-color: #e3d2d2"></span>
 history = model.fit(train_images, train_labels, epochs=<span style="color: #B452CD">10</span>, 
                    validation_data=(test_images, test_labels))
 </pre>
@@ -1302,9 +1299,7 @@ plt.xlabel(<span style="color: #CD5555">&#39;Epoch&#39;</span>)
 plt.ylabel(<span style="color: #CD5555">&#39;Accuracy&#39;</span>)
 plt.ylim([<span style="color: #B452CD">0.5</span>, <span style="color: #B452CD">1</span>])
 plt.legend(loc=<span style="color: #CD5555">&#39;lower right&#39;</span>)
-
 test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=<span style="color: #B452CD">2</span>)
-
 <span style="color: #658b00">print</span>(test_acc)
 </pre>
 </div>
@@ -1375,8 +1370,6 @@ systems such as automatic translation and speech-to-text.
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> regularizers           
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> to_categorical 

-
-
 <span style="color: #228B22"># convert into dataset matrix</span>
 <span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">convertToMatrix</span>(data, step):
 X, Y =[], []
@@ -1416,7 +1409,6 @@ model.add(Dense(<span style="color: #B452CD">8</span>, activation=<span style="c
 model.add(Dense(<span style="color: #B452CD">1</span>))
 model.compile(loss=<span style="color: #CD5555">&#39;mean_squared_error&#39;</span>, optimizer=<span style="color: #CD5555">&#39;rmsprop&#39;</span>)
 model.summary()
-
 model.fit(trainX,trainY, epochs=<span style="color: #B452CD">100</span>, batch_size=<span style="color: #B452CD">16</span>, verbose=<span style="color: #B452CD">2</span>)
 trainPredict = model.predict(trainX)
 testPredict= model.predict(testX)
@@ -1121,7 +1121,6 @@ train_images, test_images = train_images / <span style="color: #B452CD">255.0</s
        <div class="highlight" style="background: #eeeedd">
  <pre style="line-height: 125%;">class_names = [<span style="color: #CD5555">&#39;airplane&#39;</span>, <span style="color: #CD5555">&#39;automobile&#39;</span>, <span style="color: #CD5555">&#39;bird&#39;</span>, <span style="color: #CD5555">&#39;cat&#39;</span>, <span style="color: #CD5555">&#39;deer&#39;</span>,
               <span style="color: #CD5555">&#39;dog&#39;</span>, <span style="color: #CD5555">&#39;frog&#39;</span>, <span style="color: #CD5555">&#39;horse&#39;</span>, <span style="color: #CD5555">&#39;ship&#39;</span>, <span style="color: #CD5555">&#39;truck&#39;</span>]
-<span style="color: #a61717; background-color: #e3d2d2"></span>
 plt.figure(figsize=(<span style="color: #B452CD">10</span>,<span style="color: #B452CD">10</span>))
 <span style="color: #8B008B; font-weight: bold">for</span> i <span style="color: #8B008B">in</span> <span style="color: #658b00">range</span>(<span style="color: #B452CD">25</span>):
    plt.subplot(<span style="color: #B452CD">5</span>,<span style="color: #B452CD">5</span>,i+<span style="color: #B452CD">1</span>)
@@ -1212,8 +1211,7 @@ layer with 10 outputs and a softmax activation.
  <pre style="line-height: 125%;">model.add(layers.Flatten())
 model.add(layers.Dense(<span style="color: #B452CD">64</span>, activation=<span style="color: #CD5555">&#39;relu&#39;</span>))
 model.add(layers.Dense(<span style="color: #B452CD">10</span>))
-Here<span style="color: #CD5555">&#39;s the complete architecture of our model.</span>
-
+<span style="color: #228B22">#Here&#39;s the complete architecture of our model.</span>
 model.summary()
 </pre>
 </div>
@@ -1230,7 +1228,7 @@ model.summary()
  </div>
 </div>

-<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.</p>
+<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.</p>

 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="compile-and-train-the-model">Compile and train the model </h2>
@@ -1245,7 +1243,6 @@ model.summary()
  <pre style="line-height: 125%;">model.compile(optimizer=<span style="color: #CD5555">&#39;adam&#39;</span>,
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=<span style="color: #8B008B; font-weight: bold">True</span>),
              metrics=[<span style="color: #CD5555">&#39;accuracy&#39;</span>])
-<span style="color: #a61717; background-color: #e3d2d2"></span>
 history = model.fit(train_images, train_labels, epochs=<span style="color: #B452CD">10</span>, 
                    validation_data=(test_images, test_labels))
 </pre>
@@ -1280,9 +1277,7 @@ plt.xlabel(<span style="color: #CD5555">&#39;Epoch&#39;</span>)
 plt.ylabel(<span style="color: #CD5555">&#39;Accuracy&#39;</span>)
 plt.ylim([<span style="color: #B452CD">0.5</span>, <span style="color: #B452CD">1</span>])
 plt.legend(loc=<span style="color: #CD5555">&#39;lower right&#39;</span>)
-
 test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=<span style="color: #B452CD">2</span>)
-
 <span style="color: #658b00">print</span>(test_acc)
 </pre>
 </div>
@@ -1351,8 +1346,6 @@ systems such as automatic translation and speech-to-text.
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras</span> <span style="color: #8B008B; font-weight: bold">import</span> regularizers           
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">tensorflow.keras.utils</span> <span style="color: #8B008B; font-weight: bold">import</span> to_categorical 

-
-
 <span style="color: #228B22"># convert into dataset matrix</span>
 <span style="color: #8B008B; font-weight: bold">def</span> <span style="color: #008b45">convertToMatrix</span>(data, step):
 X, Y =[], []
@@ -1392,7 +1385,6 @@ model.add(Dense(<span style="color: #B452CD">8</span>, activation=<span style="c
 model.add(Dense(<span style="color: #B452CD">1</span>))
 model.compile(loss=<span style="color: #CD5555">&#39;mean_squared_error&#39;</span>, optimizer=<span style="color: #CD5555">&#39;rmsprop&#39;</span>)
 model.summary()
-
 model.fit(trainX,trainY, epochs=<span style="color: #B452CD">100</span>, batch_size=<span style="color: #B452CD">16</span>, verbose=<span style="color: #B452CD">2</span>)
 trainPredict = model.predict(trainX)
 testPredict= model.predict(testX)
@@ -1198,7 +1198,6 @@ train_images, test_images <span style="color: #666666">=</span> train_images <sp
        <div class="highlight" style="background: #f8f8f8">
  <pre style="line-height: 125%;">class_names <span style="color: #666666">=</span> [<span style="color: #BA2121">&#39;airplane&#39;</span>, <span style="color: #BA2121">&#39;automobile&#39;</span>, <span style="color: #BA2121">&#39;bird&#39;</span>, <span style="color: #BA2121">&#39;cat&#39;</span>, <span style="color: #BA2121">&#39;deer&#39;</span>,
               <span style="color: #BA2121">&#39;dog&#39;</span>, <span style="color: #BA2121">&#39;frog&#39;</span>, <span style="color: #BA2121">&#39;horse&#39;</span>, <span style="color: #BA2121">&#39;ship&#39;</span>, <span style="color: #BA2121">&#39;truck&#39;</span>]
-
 plt<span style="color: #666666">.</span>figure(figsize<span style="color: #666666">=</span>(<span style="color: #666666">10</span>,<span style="color: #666666">10</span>))
 <span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">25</span>):
    plt<span style="color: #666666">.</span>subplot(<span style="color: #666666">5</span>,<span style="color: #666666">5</span>,i<span style="color: #666666">+1</span>)
@@ -1289,8 +1288,7 @@ layer with 10 outputs and a softmax activation.
  <pre style="line-height: 125%;">model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Flatten())
 model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(<span style="color: #666666">64</span>, activation<span style="color: #666666">=</span><span style="color: #BA2121">&#39;relu&#39;</span>))
 model<span style="color: #666666">.</span>add(layers<span style="color: #666666">.</span>Dense(<span style="color: #666666">10</span>))
-Here<span style="color: #BA2121">&#39;s the complete architecture of our model.</span>
-
+<span style="color: #408080; font-style: italic">#Here&#39;s the complete architecture of our model.</span>
 model<span style="color: #666666">.</span>summary()
 </pre>
 </div>
@@ -1307,7 +1305,7 @@ model<span style="color: #666666">.</span>summary()
  </div>
 </div>

-<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.</p>
+<p>As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.</p>

 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="compile-and-train-the-model">Compile and train the model </h2>
@@ -1322,7 +1320,6 @@ model<span style="color: #666666">.</span>summary()
  <pre style="line-height: 125%;">model<span style="color: #666666">.</span>compile(optimizer<span style="color: #666666">=</span><span style="color: #BA2121">&#39;adam&#39;</span>,
              loss<span style="color: #666666">=</span>tf<span style="color: #666666">.</span>keras<span style="color: #666666">.</span>losses<span style="color: #666666">.</span>SparseCategoricalCrossentropy(from_logits<span style="color: #666666">=</span><span style="color: #008000; font-weight: bold">True</span>),
              metrics<span style="color: #666666">=</span>[<span style="color: #BA2121">&#39;accuracy&#39;</span>])
-
 history <span style="color: #666666">=</span> model<span style="color: #666666">.</span>fit(train_images, train_labels, epochs<span style="color: #666666">=10</span>, 
                    validation_data<span style="color: #666666">=</span>(test_images, test_labels))
 </pre>
@@ -1357,9 +1354,7 @@ plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39
 plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;Accuracy&#39;</span>)
 plt<span style="color: #666666">.</span>ylim([<span style="color: #666666">0.5</span>, <span style="color: #666666">1</span>])
 plt<span style="color: #666666">.</span>legend(loc<span style="color: #666666">=</span><span style="color: #BA2121">&#39;lower right&#39;</span>)
-
 test_loss, test_acc <span style="color: #666666">=</span> model<span style="color: #666666">.</span>evaluate(test_images,  test_labels, verbose<span style="color: #666666">=2</span>)
-
 <span style="color: #008000">print</span>(test_acc)
 </pre>
 </div>
@@ -1428,8 +1423,6 @@ systems such as automatic translation and speech-to-text.
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras</span> <span style="color: #008000; font-weight: bold">import</span> regularizers           
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">tensorflow.keras.utils</span> <span style="color: #008000; font-weight: bold">import</span> to_categorical 

-
-
 <span style="color: #408080; font-style: italic"># convert into dataset matrix</span>
 <span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">convertToMatrix</span>(data, step):
 X, Y <span style="color: #666666">=</span>[], []
@@ -1469,7 +1462,6 @@ model<span style="color: #666666">.</span>add(Dense(<span style="color: #666666"
 model<span style="color: #666666">.</span>add(Dense(<span style="color: #666666">1</span>))
 model<span style="color: #666666">.</span>compile(loss<span style="color: #666666">=</span><span style="color: #BA2121">&#39;mean_squared_error&#39;</span>, optimizer<span style="color: #666666">=</span><span style="color: #BA2121">&#39;rmsprop&#39;</span>)
 model<span style="color: #666666">.</span>summary()
-
 model<span style="color: #666666">.</span>fit(trainX,trainY, epochs<span style="color: #666666">=100</span>, batch_size<span style="color: #666666">=16</span>, verbose<span style="color: #666666">=2</span>)
 trainPredict <span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(trainX)
 testPredict<span style="color: #666666">=</span> model<span style="color: #666666">.</span>predict(testX)
@@ -745,7 +745,6 @@ To verify that the dataset looks correct, let's plot the first 25 images from th
 !bc pycod
 class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
               'dog', 'frog', 'horse', 'ship', 'truck']
-
 plt.figure(figsize=(10,10))
 for i in range(25):
    plt.subplot(5,5,i+1)
@@ -799,11 +798,10 @@ layer with 10 outputs and a softmax activation.
 model.add(layers.Flatten())
 model.add(layers.Dense(64, activation='relu'))
 model.add(layers.Dense(10))
-Here's the complete architecture of our model.
-
+#Here's the complete architecture of our model.
 model.summary()
 !ec
-As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.
+As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two dense layers.

 !split
 ===== Compile and train the model =====
@@ -812,7 +810,6 @@ As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (102
 model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
-
 history = model.fit(train_images, train_labels, epochs=10, 
                    validation_data=(test_images, test_labels))

@@ -829,11 +826,8 @@ plt.xlabel('Epoch')
 plt.ylabel('Accuracy')
 plt.ylim([0.5, 1])
 plt.legend(loc='lower right')
-
 test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
-
 print(test_acc)
-
 !ec


@@ -880,8 +874,6 @@ from tensorflow.keras import optimizers
 from tensorflow.keras import regularizers           
 from tensorflow.keras.utils import to_categorical 

-
-
 # convert into dataset matrix
 def convertToMatrix(data, step):
 X, Y =[], []
@@ -921,7 +913,6 @@ model.add(Dense(8, activation="relu"))
 model.add(Dense(1))
 model.compile(loss='mean_squared_error', optimizer='rmsprop')
 model.summary()
-
 model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)
 trainPredict = model.predict(trainX)
 testPredict= model.predict(testX)