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<div class="cell markdown" data-_uuid="1b1c16628c2f62a18e1dc2068e1d67d7003922b1">
</div>
<div class="cell code" data-execution_count="1" data-_uuid="2defa674e4e6d0e7371df92e7d1f388fc5c14bb6" data-execution="{"iopub.status.busy":"2022-01-07T11:32:17.618131Z","shell.execute_reply.started":"2022-01-07T11:32:17.618670Z","iopub.status.idle":"2022-01-07T11:32:20.744909Z","iopub.execute_input":"2022-01-07T11:32:17.618735Z","shell.execute_reply":"2022-01-07T11:32:20.743803Z"}" data-trusted="true">
<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.model_selection <span class="im">import</span> train_test_split</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.metrics <span class="im">import</span> classification_report</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tensorflow.python <span class="im">import</span> keras</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tensorflow.python.keras.models <span class="im">import</span> Sequential</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tensorflow.python.keras.layers <span class="im">import</span> Dense, Flatten, Conv2D, Dropout, MaxPooling2D</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> IPython.display <span class="im">import</span> SVG</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> keras.utils.vis_utils <span class="im">import</span> model_to_dot</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> keras.utils <span class="im">import</span> plot_model</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> seaborn <span class="im">as</span> sns</span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>matplotlib inline </span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.graph_objs <span class="im">as</span> go</span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.figure_factory <span class="im">as</span> ff</span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> plotly <span class="im">import</span> tools</span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> plotly.offline <span class="im">import</span> download_plotlyjs, init_notebook_mode, plot, iplot</span>
<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>init_notebook_mode(connected<span class="op">=</span><span class="va">True</span>)</span></code></pre></div>
</div>
<section id="parameters" class="cell markdown" data-_uuid="e17fd2539412442ddb3ecacf84366ddd62faf836">
<h2>Parameters</h2>
</section>
<div class="cell code" data-execution_count="2" data-_uuid="d7a44bfc7a28df7026241c4a7e047298446f1554" data-execution="{"iopub.status.busy":"2022-01-07T11:32:20.749757Z","shell.execute_reply.started":"2022-01-07T11:32:20.750039Z","iopub.status.idle":"2022-01-07T11:32:20.767535Z","iopub.execute_input":"2022-01-07T11:32:20.750323Z","shell.execute_reply":"2022-01-07T11:32:20.766340Z"}" data-trusted="true">
<div class="sourceCode" id="cb2"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>IMG_ROWS <span class="op">=</span> <span class="dv">28</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>IMG_COLS <span class="op">=</span> <span class="dv">28</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>NUM_CLASSES <span class="op">=</span> <span class="dv">10</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>TEST_SIZE <span class="op">=</span> <span class="fl">0.2</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>RANDOM_STATE <span class="op">=</span> <span class="dv">2018</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="co">#Model</span></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a>NO_EPOCHS <span class="op">=</span> <span class="dv">50</span></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>BATCH_SIZE <span class="op">=</span> <span class="dv">128</span></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a>IS_LOCAL <span class="op">=</span> <span class="va">False</span></span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span>(IS_LOCAL):</span>
<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a> PATH<span class="op">=</span><span class="st">"../input/fashionmnist/"</span></span>
<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a><span class="cf">else</span>:</span>
<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a> PATH<span class="op">=</span><span class="st">"../input/"</span></span>
<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(os.listdir(PATH))</span></code></pre></div>
</div>
<div class="cell code" data-execution_count="3" data-_uuid="a9c3148fda056ecc88570b302f5185064d5e9fc8" data-execution="{"iopub.status.busy":"2022-01-07T11:32:20.770321Z","shell.execute_reply.started":"2022-01-07T11:32:20.771025Z","iopub.status.idle":"2022-01-07T11:32:32.200740Z","iopub.execute_input":"2022-01-07T11:32:20.771114Z","shell.execute_reply":"2022-01-07T11:32:32.199557Z"}" data-trusted="true">
<div class="sourceCode" id="cb3"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>train_file <span class="op">=</span> PATH<span class="op">+</span><span class="st">"fashion-mnist_train.csv"</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>test_file <span class="op">=</span> PATH<span class="op">+</span><span class="st">"fashion-mnist_test.csv"</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>train_data <span class="op">=</span> pd.read_csv(train_file)</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>test_data <span class="op">=</span> pd.read_csv(test_file)</span></code></pre></div>
</div>
<div class="cell code" data-execution_count="4" data-_uuid="a133496cefc53baa56b9a5eec93bfd064ea6360d" data-execution="{"iopub.status.busy":"2022-01-07T11:33:09.641140Z","shell.execute_reply.started":"2022-01-07T11:33:09.641396Z","iopub.status.idle":"2022-01-07T11:33:09.648571Z","iopub.execute_input":"2022-01-07T11:33:09.641460Z","shell.execute_reply":"2022-01-07T11:33:09.647591Z"}" data-trusted="true">
<div class="sourceCode" id="cb4"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"Fashion MNIST train - rows:"</span>,train_data.shape[<span class="dv">0</span>],<span class="st">" columns:"</span>, train_data.shape[<span class="dv">1</span>])</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"Fashion MNIST test - rows:"</span>,test_data.shape[<span class="dv">0</span>],<span class="st">" columns:"</span>, test_data.shape[<span class="dv">1</span>])</span></code></pre></div>
</div>
<div class="cell code" data-execution_count="9" data-_uuid="db4c308b9fb334a54b4c055b00d2a1fbcf77ab96" data-execution="{"iopub.status.busy":"2022-01-07T11:34:20.488697Z","shell.execute_reply.started":"2022-01-07T11:34:20.489287Z","iopub.status.idle":"2022-01-07T11:34:20.496870Z","iopub.execute_input":"2022-01-07T11:34:20.489355Z","shell.execute_reply":"2022-01-07T11:34:20.495966Z"}" data-trusted="true">
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># data preprocessing</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> data_preprocessing(raw):</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> out_y <span class="op">=</span> keras.utils.to_categorical(raw.label, NUM_CLASSES)</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> num_images <span class="op">=</span> raw.shape[<span class="dv">0</span>]</span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> x_as_array <span class="op">=</span> raw.values[:,<span class="dv">1</span>:]</span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> x_shaped_array <span class="op">=</span> x_as_array.reshape(num_images, IMG_ROWS, IMG_COLS, <span class="dv">1</span>)</span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> out_x <span class="op">=</span> x_shaped_array <span class="op">/</span> <span class="dv">255</span></span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> out_x, out_y</span></code></pre></div>
</div>
<div class="cell markdown" data-_uuid="2d3cafa55173d40cd9a42df63d4919f03b264c09">
<p>We process both the train_data and the test_data</p>
</div>
<div class="cell code" data-execution_count="10" data-_uuid="454d7a8fdca4bdbefc04a9d796de04b6af4a1767" data-execution="{"iopub.status.busy":"2022-01-07T11:34:34.155344Z","shell.execute_reply.started":"2022-01-07T11:34:34.155756Z","iopub.status.idle":"2022-01-07T11:34:34.540659Z","iopub.execute_input":"2022-01-07T11:34:34.155815Z","shell.execute_reply":"2022-01-07T11:34:34.539868Z"}" data-trusted="true">
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># prepare the data</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>X, y <span class="op">=</span> data_preprocessing(train_data)</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>X_test, y_test <span class="op">=</span> data_preprocessing(test_data)</span></code></pre></div>
</div>
<section id="split-train-in-train-and-validation-set" class="cell markdown" data-_uuid="8e25c569f0a0e817ca0462f3e3ea2f50feb480b0">
<h2>Split train in train and validation set</h2>
<p>We further split the train set in train and validation set. The validation set will be 20% from the original train set, therefore the split will be train/validation of 0.8/0.2.</p>
</section>
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<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>X_train, X_val, y_train, y_val <span class="op">=</span> train_test_split(X, y, test_size<span class="op">=</span>TEST_SIZE, random_state<span class="op">=</span>RANDOM_STATE)</span></code></pre></div>
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<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"Fashion MNIST train - rows:"</span>,X_train.shape[<span class="dv">0</span>],<span class="st">" columns:"</span>, X_train.shape[<span class="dv">1</span>:<span class="dv">4</span>])</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"Fashion MNIST valid - rows:"</span>,X_val.shape[<span class="dv">0</span>],<span class="st">" columns:"</span>, X_val.shape[<span class="dv">1</span>:<span class="dv">4</span>])</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"Fashion MNIST test - rows:"</span>,X_test.shape[<span class="dv">0</span>],<span class="st">" columns:"</span>, X_test.shape[<span class="dv">1</span>:<span class="dv">4</span>])</span></code></pre></div>
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<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Model</span></span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> Sequential()</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="co"># Add convolution 2D</span></span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>model.add(Conv2D(<span class="dv">32</span>, kernel_size<span class="op">=</span>(<span class="dv">3</span>, <span class="dv">3</span>),</span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> activation<span class="op">=</span><span class="st">'relu'</span>,</span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a> kernel_initializer<span class="op">=</span><span class="st">'he_normal'</span>,</span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a> input_shape<span class="op">=</span>(IMG_ROWS, IMG_COLS, <span class="dv">1</span>)))</span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>model.add(MaxPooling2D((<span class="dv">2</span>, <span class="dv">2</span>)))</span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a>model.add(Conv2D(<span class="dv">64</span>, </span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a> kernel_size<span class="op">=</span>(<span class="dv">3</span>, <span class="dv">3</span>), </span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a> activation<span class="op">=</span><span class="st">'relu'</span>))</span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a>model.add(MaxPooling2D(pool_size<span class="op">=</span>(<span class="dv">2</span>, <span class="dv">2</span>)))</span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a>model.add(Conv2D(<span class="dv">128</span>, (<span class="dv">3</span>, <span class="dv">3</span>), activation<span class="op">=</span><span class="st">'relu'</span>))</span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a>model.add(Flatten())</span>
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a>model.add(Dense(<span class="dv">128</span>, activation<span class="op">=</span><span class="st">'relu'</span>))</span>
<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a>model.add(Dense(NUM_CLASSES, activation<span class="op">=</span><span class="st">'softmax'</span>))</span>
<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a>model.<span class="bu">compile</span>(loss<span class="op">=</span>keras.losses.categorical_crossentropy,</span>
<span id="cb9-20"><a href="#cb9-20" aria-hidden="true" tabindex="-1"></a> optimizer<span class="op">=</span><span class="st">'adam'</span>,</span>
<span id="cb9-21"><a href="#cb9-21" aria-hidden="true" tabindex="-1"></a> metrics<span class="op">=</span>[<span class="st">'accuracy'</span>])</span></code></pre></div>
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<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>model.summary()</span></code></pre></div>
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<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>train_model <span class="op">=</span> model.fit(X_train, y_train,</span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> batch_size<span class="op">=</span>BATCH_SIZE,</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> epochs<span class="op">=</span>NO_EPOCHS,</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> verbose<span class="op">=</span><span class="dv">1</span>,</span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> validation_data<span class="op">=</span>(X_val, y_val))</span></code></pre></div>
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<div class="sourceCode" id="cb12"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>score <span class="op">=</span> model.evaluate(X_test, y_test, verbose<span class="op">=</span><span class="dv">0</span>)</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">'Test loss:'</span>, score[<span class="dv">0</span>])</span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">'Test accuracy:'</span>, score[<span class="dv">1</span>])</span></code></pre></div>
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