<p dir="auto">I'm trying to get TensorBoard working with Keras.<br>
It looks like I'm able to run an initial model with tf (1.12.0) and keras (2.1.6-tf). I have some simple code. Listed below:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="%matplotlib inline
from io import StringIO

import numpy as np
import pandas as pd
import tensorflow as tf

csv = StringIO('''a,b,c,y
0,1,2,0
1,2,0,1
0,2,1,0
3,2,1,1
3,1,2,0''')
data = pd.read_csv(csv)

def tb_cb(batch_size):
    # visualize graphs and grandient
    tb = tf.keras.callbacks.TensorBoard(log_dir='/tmp/test/',
                                       histogram_freq=1,
                                       batch_size=batch_size, write_graph=True,
                                       write_grads=True)
    return tb

m = tf.keras.Sequential([
   # going to change 1 in the line below
    tf.keras.layers.Dense(1, activation='relu', input_shape=(3,), name='hidden1'),
    tf.keras.layers.Dense(1, activation='linear', name='output')
])
m.compile(loss='mse', optimizer='adam', metrics=['mae'])
X = data.iloc[:,:3]
y = data.y
hist = m.fit(X, y, epochs=10, verbose=1, callbacks=[tb_cb(10)],
            validation_data=(X,y))"><pre class="notranslate"><span class="pl-c1">%</span><span class="pl-s1">matplotlib</span> <span class="pl-s1">inline</span>
<span class="pl-k">from</span> <span class="pl-s1">io</span> <span class="pl-k">import</span> <span class="pl-v">StringIO</span>

<span class="pl-k">import</span> <span class="pl-s1">numpy</span> <span class="pl-k">as</span> <span class="pl-s1">np</span>
<span class="pl-k">import</span> <span class="pl-s1">pandas</span> <span class="pl-k">as</span> <span class="pl-s1">pd</span>
<span class="pl-k">import</span> <span class="pl-s1">tensorflow</span> <span class="pl-k">as</span> <span class="pl-s1">tf</span>

<span class="pl-s1">csv</span> <span class="pl-c1">=</span> <span class="pl-v">StringIO</span>(<span class="pl-s">'''a,b,c,y</span>
<span class="pl-s">0,1,2,0</span>
<span class="pl-s">1,2,0,1</span>
<span class="pl-s">0,2,1,0</span>
<span class="pl-s">3,2,1,1</span>
<span class="pl-s">3,1,2,0'''</span>)
<span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-en">read_csv</span>(<span class="pl-s1">csv</span>)

<span class="pl-k">def</span> <span class="pl-en">tb_cb</span>(<span class="pl-s1">batch_size</span>):
    <span class="pl-c"># visualize graphs and grandient</span>
    <span class="pl-s1">tb</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">keras</span>.<span class="pl-s1">callbacks</span>.<span class="pl-v">TensorBoard</span>(<span class="pl-s1">log_dir</span><span class="pl-c1">=</span><span class="pl-s">'/tmp/test/'</span>,
                                       <span class="pl-s1">histogram_freq</span><span class="pl-c1">=</span><span class="pl-c1">1</span>,
                                       <span class="pl-s1">batch_size</span><span class="pl-c1">=</span><span class="pl-s1">batch_size</span>, <span class="pl-s1">write_graph</span><span class="pl-c1">=</span><span class="pl-c1">True</span>,
                                       <span class="pl-s1">write_grads</span><span class="pl-c1">=</span><span class="pl-c1">True</span>)
    <span class="pl-k">return</span> <span class="pl-s1">tb</span>

<span class="pl-s1">m</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">keras</span>.<span class="pl-v">Sequential</span>([
   <span class="pl-c"># going to change 1 in the line below</span>
    <span class="pl-s1">tf</span>.<span class="pl-s1">keras</span>.<span class="pl-s1">layers</span>.<span class="pl-v">Dense</span>(<span class="pl-c1">1</span>, <span class="pl-s1">activation</span><span class="pl-c1">=</span><span class="pl-s">'relu'</span>, <span class="pl-s1">input_shape</span><span class="pl-c1">=</span>(<span class="pl-c1">3</span>,), <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">'hidden1'</span>),
    <span class="pl-s1">tf</span>.<span class="pl-s1">keras</span>.<span class="pl-s1">layers</span>.<span class="pl-v">Dense</span>(<span class="pl-c1">1</span>, <span class="pl-s1">activation</span><span class="pl-c1">=</span><span class="pl-s">'linear'</span>, <span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">'output'</span>)
])
<span class="pl-s1">m</span>.<span class="pl-en">compile</span>(<span class="pl-s1">loss</span><span class="pl-c1">=</span><span class="pl-s">'mse'</span>, <span class="pl-s1">optimizer</span><span class="pl-c1">=</span><span class="pl-s">'adam'</span>, <span class="pl-s1">metrics</span><span class="pl-c1">=</span>[<span class="pl-s">'mae'</span>])
<span class="pl-v">X</span> <span class="pl-c1">=</span> <span class="pl-s1">data</span>.<span class="pl-s1">iloc</span>[:,:<span class="pl-c1">3</span>]
<span class="pl-s1">y</span> <span class="pl-c1">=</span> <span class="pl-s1">data</span>.<span class="pl-s1">y</span>
<span class="pl-s1">hist</span> <span class="pl-c1">=</span> <span class="pl-s1">m</span>.<span class="pl-en">fit</span>(<span class="pl-v">X</span>, <span class="pl-s1">y</span>, <span class="pl-s1">epochs</span><span class="pl-c1">=</span><span class="pl-c1">10</span>, <span class="pl-s1">verbose</span><span class="pl-c1">=</span><span class="pl-c1">1</span>, <span class="pl-s1">callbacks</span><span class="pl-c1">=</span>[<span class="pl-en">tb_cb</span>(<span class="pl-c1">10</span>)],
            <span class="pl-s1">validation_data</span><span class="pl-c1">=</span>(<span class="pl-v">X</span>,<span class="pl-s1">y</span>))</pre></div>
<p dir="auto">The first time I run this I get TensorBoard output. I have then changed the number of neurons in the hidden layer and re-run the model.</p>
<p dir="auto">I get the following error:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
&lt;ipython-input-11-4e4fb6f60bf0&gt; in &lt;module&gt;
     15 y = data.y
     16 hist = m.fit(X, y, epochs=10, verbose=1, callbacks=[tb_cb(10)],
---&gt; 17             validation_data=(X,y))

~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)
   1637           initial_epoch=initial_epoch,
   1638           steps_per_epoch=steps_per_epoch,
-&gt; 1639           validation_steps=validation_steps)
   1640 
   1641   def evaluate(self,

~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py in fit_loop(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)
    231                 sample_weights=val_sample_weights,
    232                 batch_size=batch_size,
--&gt; 233                 verbose=0)
    234             if not isinstance(val_outs, list):
    235               val_outs = [val_outs]

~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py in test_loop(model, inputs, targets, sample_weights, batch_size, verbose, steps)
    437         ins_batch[i] = ins_batch[i].toarray()
    438 
--&gt; 439       batch_outs = f(ins_batch)
    440 
    441       if isinstance(batch_outs, list):

~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/backend.py in __call__(self, inputs)
   2984 
   2985     fetched = self._callable_fn(*array_vals,
-&gt; 2986                                 run_metadata=self.run_metadata)
   2987     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   2988     return fetched[:len(self.outputs)]

~/.env/364/lib/python3.6/site-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
   1437           ret = tf_session.TF_SessionRunCallable(
   1438               self._session._session, self._handle, args, status,
-&gt; 1439               run_metadata_ptr)
   1440         if run_metadata:
   1441           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/.env/364/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    526             None, None,
    527             compat.as_text(c_api.TF_Message(self.status.status)),
--&gt; 528             c_api.TF_GetCode(self.status.status))
    529     # Delete the underlying status object from memory otherwise it stays alive
    530     # as there is a reference to status from this from the traceback due to

InvalidArgumentError: You must feed a value for placeholder tensor 'dense_9_target' with dtype float and shape [?,?]
	 [[{{node dense_9_target}} = Placeholder[dtype=DT_FLOAT, shape=[?,?], _device=&quot;/job:localhost/replica:0/task:0/device:CPU:0&quot;]()]]"><pre class="notranslate"><code class="notranslate">---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
&lt;ipython-input-11-4e4fb6f60bf0&gt; in &lt;module&gt;
     15 y = data.y
     16 hist = m.fit(X, y, epochs=10, verbose=1, callbacks=[tb_cb(10)],
---&gt; 17             validation_data=(X,y))

~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)
   1637           initial_epoch=initial_epoch,
   1638           steps_per_epoch=steps_per_epoch,
-&gt; 1639           validation_steps=validation_steps)
   1640 
   1641   def evaluate(self,

~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py in fit_loop(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)
    231                 sample_weights=val_sample_weights,
    232                 batch_size=batch_size,
--&gt; 233                 verbose=0)
    234             if not isinstance(val_outs, list):
    235               val_outs = [val_outs]

~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py in test_loop(model, inputs, targets, sample_weights, batch_size, verbose, steps)
    437         ins_batch[i] = ins_batch[i].toarray()
    438 
--&gt; 439       batch_outs = f(ins_batch)
    440 
    441       if isinstance(batch_outs, list):

~/.env/364/lib/python3.6/site-packages/tensorflow/python/keras/backend.py in __call__(self, inputs)
   2984 
   2985     fetched = self._callable_fn(*array_vals,
-&gt; 2986                                 run_metadata=self.run_metadata)
   2987     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   2988     return fetched[:len(self.outputs)]

~/.env/364/lib/python3.6/site-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
   1437           ret = tf_session.TF_SessionRunCallable(
   1438               self._session._session, self._handle, args, status,
-&gt; 1439               run_metadata_ptr)
   1440         if run_metadata:
   1441           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/.env/364/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    526             None, None,
    527             compat.as_text(c_api.TF_Message(self.status.status)),
--&gt; 528             c_api.TF_GetCode(self.status.status))
    529     # Delete the underlying status object from memory otherwise it stays alive
    530     # as there is a reference to status from this from the traceback due to

InvalidArgumentError: You must feed a value for placeholder tensor 'dense_9_target' with dtype float and shape [?,?]
	 [[{{node dense_9_target}} = Placeholder[dtype=DT_FLOAT, shape=[?,?], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
</code></pre></div>
<p dir="auto">TensorBoard in Keras crashes deep in the callbacks for the end-of-epoch. Here's a simplified version of the code:</p>
<h1 dir="auto">The Code</h1>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="import pandas as pd
import keras
from keras.models import Sequential
from keras.layers import Dense

class ToyNet ():
    def __init__(self, run=1, layer_1_nodes=50, layer_2_nodes=100, layer_3_nodes=50):
        self.run_seq = run
        self.layer_1_nodes = layer_1_nodes
        self.layer_2_nodes = layer_2_nodes
        self.layer_3_nodes = layer_3_nodes

        # Define the model
        self.model = Sequential()
        self.model.add(Dense(self.layer_1_nodes, input_dim=9, activation='relu', name='layer_1'))
        self.model.add(Dense(self.layer_2_nodes, activation='relu', name='layer_2'))
        self.model.add(Dense(self.layer_3_nodes, activation='relu', name='layer_3'))
        self.model.add(Dense(1, activation='linear', name='output_layer'))
        self.model.compile(loss='mean_squared_error', optimizer='adam')

        # Create a TensorBoard logger
        log_dir = &quot;logs_{}&quot;.format(self.run_seq)
        self.logger = keras.callbacks.TensorBoard(
            log_dir=log_dir,
            histogram_freq=5
        )

    def train(self, X, Y, epochs=50):
        # Train the model
        self.model.fit(
            X,
            Y,
            epochs=epochs,
            shuffle=True,
            verbose=2,
            validation_split=0.05,
            callbacks=[self.logger]
        )

    def test(self, Xt, Yt):
        test_error_rate = self.model.evaluate(Xt, Yt, verbose=0)
        print(&quot;The mean squared error (MSE) for the test data set is: {}&quot;.format(test_error_rate))

if __name__ == '__main__':

    training_data_df = pd.read_csv(&quot;sales_data_training_scaled.csv&quot;)

    X = training_data_df.drop('total_earnings', axis=1).values
    Y = training_data_df[['total_earnings']].values

    # Load the test data set
    test_data_df = pd.read_csv(&quot;sales_data_test_scaled.csv&quot;)

    X_test = test_data_df.drop('total_earnings', axis=1).values
    Y_test = test_data_df[['total_earnings']].values

    print(&quot;Run #1&quot;)
    toy1 = ToyNet(1, 50, 100, 50)
    toy1.train(X, Y)
    toy1.test(X_test, Y_test)

    print(&quot;Run #2&quot;)
    toy2 = ToyNet(2, 5, 100, 50)
    toy2.train(X,Y)              ### &lt;--- Crashes here at the end of first epoch while doing callbacks
    toy2.test(X_test, Y_test)"><pre class="notranslate"><code class="notranslate">import pandas as pd
import keras
from keras.models import Sequential
from keras.layers import Dense

class ToyNet ():
    def __init__(self, run=1, layer_1_nodes=50, layer_2_nodes=100, layer_3_nodes=50):
        self.run_seq = run
        self.layer_1_nodes = layer_1_nodes
        self.layer_2_nodes = layer_2_nodes
        self.layer_3_nodes = layer_3_nodes

        # Define the model
        self.model = Sequential()
        self.model.add(Dense(self.layer_1_nodes, input_dim=9, activation='relu', name='layer_1'))
        self.model.add(Dense(self.layer_2_nodes, activation='relu', name='layer_2'))
        self.model.add(Dense(self.layer_3_nodes, activation='relu', name='layer_3'))
        self.model.add(Dense(1, activation='linear', name='output_layer'))
        self.model.compile(loss='mean_squared_error', optimizer='adam')

        # Create a TensorBoard logger
        log_dir = "logs_{}".format(self.run_seq)
        self.logger = keras.callbacks.TensorBoard(
            log_dir=log_dir,
            histogram_freq=5
        )

    def train(self, X, Y, epochs=50):
        # Train the model
        self.model.fit(
            X,
            Y,
            epochs=epochs,
            shuffle=True,
            verbose=2,
            validation_split=0.05,
            callbacks=[self.logger]
        )

    def test(self, Xt, Yt):
        test_error_rate = self.model.evaluate(Xt, Yt, verbose=0)
        print("The mean squared error (MSE) for the test data set is: {}".format(test_error_rate))

if __name__ == '__main__':

    training_data_df = pd.read_csv("sales_data_training_scaled.csv")

    X = training_data_df.drop('total_earnings', axis=1).values
    Y = training_data_df[['total_earnings']].values

    # Load the test data set
    test_data_df = pd.read_csv("sales_data_test_scaled.csv")

    X_test = test_data_df.drop('total_earnings', axis=1).values
    Y_test = test_data_df[['total_earnings']].values

    print("Run #1")
    toy1 = ToyNet(1, 50, 100, 50)
    toy1.train(X, Y)
    toy1.test(X_test, Y_test)

    print("Run #2")
    toy2 = ToyNet(2, 5, 100, 50)
    toy2.train(X,Y)              ### &lt;--- Crashes here at the end of first epoch while doing callbacks
    toy2.test(X_test, Y_test)
</code></pre></div>
<h1 dir="auto">The Output</h1>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/h5py/<strong>init</strong>.py:36: FutureWarning: Conversion of the second argument of issubdtype from <code class="notranslate">float</code> to <code class="notranslate">np.floating</code> is deprecated. In future, it will be treated as <code class="notranslate">np.float64 == np.dtype(float).type</code>.<br>
from ._conv import register_converters as _register_converters<br>
Using TensorFlow backend.<br>
Run <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="64886719" data-permission-text="Title is private" data-url="https://github.com/keras-team/keras/issues/1" data-hovercard-type="pull_request" data-hovercard-url="/keras-team/keras/pull/1/hovercard" href="https://github.com/keras-team/keras/pull/1">#1</a><br>
Train on 950 samples, validate on 50 samples<br>
Epoch 1/50</p>
<ul dir="auto">
<li>1s - loss: 0.0314 - val_loss: 0.0043<br>
Epoch 2/50</li>
<li>0s - loss: 0.0048 - val_loss: 0.0011</li>
</ul>
<blockquote>
<p dir="auto">... output snipped for brevity</p>
</blockquote>
<p dir="auto">Epoch 50/50</p>
<ul dir="auto">
<li>0s - loss: 2.4237e-05 - val_loss: 5.0361e-05<br>
The mean squared error (MSE) for the test data set is: 7.575784547952935e-05<br>
Run <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="64933940" data-permission-text="Title is private" data-url="https://github.com/keras-team/keras/issues/2" data-hovercard-type="pull_request" data-hovercard-url="/keras-team/keras/pull/2/hovercard" href="https://github.com/keras-team/keras/pull/2">#2</a><br>
Train on 950 samples, validate on 50 samples<br>
Epoch 1/50</li>
<li>1s - loss: 0.0333 - val_loss: 0.0172</li>
</ul>
<hr>
<p dir="auto">InvalidArgumentError                      Traceback (most recent call last)<br>
/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)<br>
1321     try:<br>
-&gt; 1322       return fn(*args)<br>
1323     except errors.OpError as e:</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)<br>
1306       return self._call_tf_sessionrun(<br>
-&gt; 1307           options, feed_dict, fetch_list, target_list, run_metadata)<br>
1308</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)<br>
1408           self._session, options, feed_dict, fetch_list, target_list,<br>
-&gt; 1409           run_metadata)<br>
1410     else:</p>
<p dir="auto">InvalidArgumentError: You must feed a value for placeholder tensor 'layer_1_input' with dtype float and shape [?,9]<br>
[[Node: layer_1_input = Placeholder<a href="">dtype=DT_FLOAT, shape=[?,9], _device="/job:localhost/replica:0/task:0/device:CPU:0"</a>]]</p>
<p dir="auto">During handling of the above exception, another exception occurred:</p>
<p dir="auto">InvalidArgumentError                      Traceback (most recent call last)<br>
 in ()<br>
64<br>
65     toy2 = ToyNet(2, 5, 100, 50)<br>
---&gt; 66     toy2.train(X,Y)              ### &lt;--- Crashes here at the end of first epoch while doing callbacks<br>
67     toy2.test(X_test, Y_test)</p>
<p dir="auto"> in train(self, X, Y, epochs)<br>
39             verbose=2,<br>
40             validation_split=0.05,<br>
---&gt; 41             callbacks=[self.logger]<br>
42         )<br>
43</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)<br>
1043                                         initial_epoch=initial_epoch,<br>
1044                                         steps_per_epoch=steps_per_epoch,<br>
-&gt; 1045                                         validation_steps=validation_steps)<br>
1046<br>
1047     def evaluate(self, x=None, y=None,</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)<br>
215                         for l, o in zip(out_labels, val_outs):<br>
216                             epoch_logs['val_' + l] = o<br>
--&gt; 217         callbacks.on_epoch_end(epoch, epoch_logs)<br>
218         if callback_model.stop_training:<br>
219             break</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)<br>
75         logs = logs or {}<br>
76         for callback in self.callbacks:<br>
---&gt; 77             callback.on_epoch_end(epoch, logs)<br>
78<br>
79     def on_batch_begin(self, batch, logs=None):</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)<br>
915                     assert len(batch_val) == len(tensors)<br>
916                     feed_dict = dict(zip(tensors, batch_val))<br>
--&gt; 917                     result = self.sess.run([self.merged], feed_dict=feed_dict)<br>
918                     summary_str = result[0]<br>
919                     self.writer.add_summary(summary_str, epoch)</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)<br>
898     try:<br>
899       result = self._run(None, fetches, feed_dict, options_ptr,<br>
--&gt; 900                          run_metadata_ptr)<br>
901       if run_metadata:<br>
902         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)<br>
1133     if final_fetches or final_targets or (handle and feed_dict_tensor):<br>
1134       results = self._do_run(handle, final_targets, final_fetches,<br>
-&gt; 1135                              feed_dict_tensor, options, run_metadata)<br>
1136     else:<br>
1137       results = []</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)<br>
1314     if handle is None:<br>
1315       return self._do_call(_run_fn, feeds, fetches, targets, options,<br>
-&gt; 1316                            run_metadata)<br>
1317     else:<br>
1318       return self._do_call(_prun_fn, handle, feeds, fetches)</p>
<p dir="auto">/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)<br>
1333         except KeyError:<br>
1334           pass<br>
-&gt; 1335       raise type(e)(node_def, op, message)<br>
1336<br>
1337   def _extend_graph(self):</p>
<p dir="auto">InvalidArgumentError: You must feed a value for placeholder tensor 'layer_1_input' with dtype float and shape [?,9]<br>
[[Node: layer_1_input = Placeholder<a href="">dtype=DT_FLOAT, shape=[?,9], _device="/job:localhost/replica:0/task:0/device:CPU:0"</a>]]</p>
<p dir="auto">Caused by op 'layer_1_input', defined at:<br>
File "/anaconda3/lib/python3.6/runpy.py", line 193, in _run_module_as_main<br>
"<strong>main</strong>", mod_spec)<br>
File "/anaconda3/lib/python3.6/runpy.py", line 85, in _run_code<br>
exec(code, run_globals)<br>
File "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <br>
app.launch_new_instance()<br>
File "/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance<br>
app.start()<br>
File "/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 486, in start<br>
self.io_loop.start()<br>
File "/anaconda3/lib/python3.6/site-packages/tornado/platform/asyncio.py", line 127, in start<br>
self.asyncio_loop.run_forever()<br>
File "/anaconda3/lib/python3.6/asyncio/base_events.py", line 422, in run_forever<br>
self._run_once()<br>
File "/anaconda3/lib/python3.6/asyncio/base_events.py", line 1432, in _run_once<br>
handle._run()<br>
File "/anaconda3/lib/python3.6/asyncio/events.py", line 145, in _run<br>
self._callback(*self._args)<br>
File "/anaconda3/lib/python3.6/site-packages/tornado/platform/asyncio.py", line 117, in _handle_events<br>
handler_func(fileobj, events)<br>
File "/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 276, in null_wrapper<br>
return fn(*args, **kwargs)<br>
File "/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 450, in _handle_events<br>
self._handle_recv()<br>
File "/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv<br>
self._run_callback(callback, msg)<br>
File "/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 432, in _run_callback<br>
callback(*args, **kwargs)<br>
File "/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 276, in null_wrapper<br>
return fn(*args, **kwargs)<br>
File "/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher<br>
return self.dispatch_shell(stream, msg)<br>
File "/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell<br>
handler(stream, idents, msg)<br>
File "/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request<br>
user_expressions, allow_stdin)<br>
File "/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute<br>
res = shell.run_cell(code, store_history=store_history, silent=silent)<br>
File "/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 537, in run_cell<br>
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)<br>
File "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2662, in run_cell<br>
raw_cell, store_history, silent, shell_futures)<br>
File "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2785, in _run_cell<br>
interactivity=interactivity, compiler=compiler, result=result)<br>
File "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2903, in run_ast_nodes<br>
if self.run_code(code, result):<br>
File "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2963, in run_code<br>
exec(code_obj, self.user_global_ns, self.user_ns)<br>
File "", line 61, in <br>
toy1 = ToyNet(1, 50, 100, 50)<br>
File "", line 19, in <strong>init</strong><br>
self.model.add(Dense(self.layer_1_nodes, input_dim=9, activation='relu', name='layer_1'))<br>
File "/anaconda3/lib/python3.6/site-packages/keras/engine/sequential.py", line 160, in add<br>
name=layer.name + '_input')<br>
File "/anaconda3/lib/python3.6/site-packages/keras/engine/input_layer.py", line 178, in Input<br>
input_tensor=tensor)<br>
File "/anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper<br>
return func(*args, **kwargs)<br>
File "/anaconda3/lib/python3.6/site-packages/keras/engine/input_layer.py", line 87, in <strong>init</strong><br>
name=self.name)<br>
File "/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 517, in placeholder<br>
x = tf.placeholder(dtype, shape=shape, name=name)<br>
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 1734, in placeholder<br>
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)<br>
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 4924, in placeholder<br>
"Placeholder", dtype=dtype, shape=shape, name=name)<br>
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper<br>
op_def=op_def)<br>
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3414, in create_op<br>
op_def=op_def)<br>
File "/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1740, in <strong>init</strong><br>
self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access</p>
<p dir="auto">InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'layer_1_input' with dtype float and shape [?,9]<br>
[[Node: layer_1_input = Placeholder<a href="">dtype=DT_FLOAT, shape=[?,9], _device="/job:localhost/replica:0/task:0/device:CPU:0"</a>]]</p>
<h1 dir="auto">The Data</h1>
<p dir="auto">For sanity's sake I've only included a short sample of each training and testing data below.</p>
<h2 dir="auto">Training data ("sales_data_training_scaled.csv")</h2>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price
0.4999999999999999,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.7991793127668619,1.0
0.16666666666666663,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.15750170976506905,1.0
0.4999999999999999,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.18970444169239015,1.0
0.6666666666666666,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.39223304559989647,0.0
0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.21546366980277626,1.0
0.4999999999999999,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.2675699155283636,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2418106874179775,1.0
0.4999999999999999,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.7090183175911721,1.0
0.6666666666666666,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.4385279384854254,1.0
0.16666666666666663,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.3957893569434946,1.0
0.9999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.24197334614886973,0.0
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.33607142197001905,1.0
0.4999999999999999,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.26054601578529046,1.0
0.6666666666666666,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.3501229182455038,1.0
0.8333333333333334,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.39457681004047984,0.0
0.6666666666666666,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.2248313339864328,1.0
0.4999999999999999,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.28864900833625995,1.0
0.4999999999999999,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.05268664165172547,0.0
0.9999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.24431711058945305,0.0
0.16666666666666663,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.2740097225559601,1.0
0.33333333333333337,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.20141217352729157,1.0
0.4999999999999999,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.2802240254339107,0.0
0.16666666666666663,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.4069129960629193,1.0
0.6666666666666666,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.21487957708729966,1.0
0.4999999999999999,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.4250642317147557,1.0
0.4999999999999999,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.28513336167538494,1.0
0.33333333333333337,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.40075044823570727,0.5
0.4999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.18811482227685256,0.0
0.33333333333333337,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1428661207741077,1.0
0.8333333333333334,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.27292286649045305,0.5
0.16666666666666663,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.16160144914142066,1.0
0.4999999999999999,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.5831426406166245,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.18850668194672926,0.0
0.8333333333333334,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.6118297258830706,1.0
0.9999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.18928670449714424,0.0
0.4999999999999999,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.13759449917746436,1.0
0.9999999999999999,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.34539842147095245,0.0
0.16666666666666663,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.13583113066301916,0.5
0.9999999999999999,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.48629415352766125,0.0
0.9999999999999999,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.7500009241973347,1.0
0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.04253895491765401,0.0
0.4999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.11630468937727585,0.0
0.16666666666666663,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.2775253692168352,1.0
0.4999999999999999,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.10216816694700652,0.5
0.4999999999999999,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.23650949150662648,0.0
0.33333333333333337,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.18031089998336447,0.0
0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.05621337868061589,1.0
0.9999999999999999,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.7949538825530027,0.5
0.4999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.18538289495573096,0.0
0.8333333333333334,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.2587900408495222,1.0
0.16666666666666663,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.08373227851610877,1.0
0.9999999999999999,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.7447329993900298,1.0
0.16666666666666663,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.20902386277517976,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.18850668194672926,0.0
0.16666666666666663,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.12509565442413267,0.5
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.3313875898781908,1.0
0.33333333333333337,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.3717861037688767,1.0
0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.1434502134895843,1.0
0.33333333333333337,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.6329051219016284,1.0
0.8333333333333334,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.727753645958485,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.15144267203933381,0.5
0.8333333333333334,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.8846601726400621,1.0
0.8333333333333334,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.30738433670357296,1.0"><pre class="notranslate"><code class="notranslate">critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price
0.4999999999999999,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.7991793127668619,1.0
0.16666666666666663,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.15750170976506905,1.0
0.4999999999999999,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.18970444169239015,1.0
0.6666666666666666,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.39223304559989647,0.0
0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.21546366980277626,1.0
0.4999999999999999,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.2675699155283636,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.2418106874179775,1.0
0.4999999999999999,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.7090183175911721,1.0
0.6666666666666666,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.4385279384854254,1.0
0.16666666666666663,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.3957893569434946,1.0
0.9999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.24197334614886973,0.0
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.33607142197001905,1.0
0.4999999999999999,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.26054601578529046,1.0
0.6666666666666666,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.3501229182455038,1.0
0.8333333333333334,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.39457681004047984,0.0
0.6666666666666666,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.2248313339864328,1.0
0.4999999999999999,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.28864900833625995,1.0
0.4999999999999999,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.05268664165172547,0.0
0.9999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.24431711058945305,0.0
0.16666666666666663,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.2740097225559601,1.0
0.33333333333333337,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.20141217352729157,1.0
0.4999999999999999,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.2802240254339107,0.0
0.16666666666666663,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.4069129960629193,1.0
0.6666666666666666,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.21487957708729966,1.0
0.4999999999999999,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.4250642317147557,1.0
0.4999999999999999,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.28513336167538494,1.0
0.33333333333333337,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.40075044823570727,0.5
0.4999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.18811482227685256,0.0
0.33333333333333337,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1428661207741077,1.0
0.8333333333333334,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.27292286649045305,0.5
0.16666666666666663,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.16160144914142066,1.0
0.4999999999999999,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.5831426406166245,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.18850668194672926,0.0
0.8333333333333334,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.6118297258830706,1.0
0.9999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.18928670449714424,0.0
0.4999999999999999,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.13759449917746436,1.0
0.9999999999999999,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.34539842147095245,0.0
0.16666666666666663,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.13583113066301916,0.5
0.9999999999999999,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.48629415352766125,0.0
0.9999999999999999,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.7500009241973347,1.0
0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.04253895491765401,0.0
0.4999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.11630468937727585,0.0
0.16666666666666663,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.2775253692168352,1.0
0.4999999999999999,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.10216816694700652,0.5
0.4999999999999999,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.23650949150662648,0.0
0.33333333333333337,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.18031089998336447,0.0
0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.05621337868061589,1.0
0.9999999999999999,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.7949538825530027,0.5
0.4999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.18538289495573096,0.0
0.8333333333333334,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.2587900408495222,1.0
0.16666666666666663,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.08373227851610877,1.0
0.9999999999999999,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.7447329993900298,1.0
0.16666666666666663,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.20902386277517976,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.18850668194672926,0.0
0.16666666666666663,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.12509565442413267,0.5
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.3313875898781908,1.0
0.33333333333333337,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.3717861037688767,1.0
0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.1434502134895843,1.0
0.33333333333333337,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.6329051219016284,1.0
0.8333333333333334,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.727753645958485,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.15144267203933381,0.5
0.8333333333333334,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.8846601726400621,1.0
0.8333333333333334,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.30738433670357296,1.0
</code></pre></div>
<h2 dir="auto">Testing data ("sales_data_test_scaled.csv")</h2>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price
0.4999999999999999,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.3747139609249367,1.0
0.8333333333333334,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.19242527864549636,0.5
0.33333333333333337,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.11485185116726125,0.5
0.8333333333333334,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.14245208036820023,0.0
0.6666666666666666,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.4806824273118796,1.0
0.6666666666666666,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.13972015304707863,0.0
0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.11338792258923126,0.5
0.8333333333333334,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.44906748488937354,1.0
0.4999999999999999,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.06127428328496702,0.0
0.16666666666666663,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.20668009833459638,1.0
0.8333333333333334,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.4777545701558197,1.0
0.4999999999999999,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.13232657437015954,1.0
0.4999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.17925361823256503,0.5
0.6666666666666666,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.16335742407718895,1.0
0.4999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.23946692297739414,1.0
0.6666666666666666,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.31206816879540117,1.0
0.4999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.2285281233248923,0.5
0.33333333333333337,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.29274505092327313,1.0
0.16666666666666663,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.3480564130053049,0.5
0.9999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.42799208887081575,1.0
0.6666666666666666,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.2248313339864328,1.0
0.8333333333333334,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.1280235115801926,0.5
0.16666666666666663,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.40164507125561455,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.2963420269495943,0.5
0.8333333333333334,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.2486090830114046,0.0
0.8333333333333334,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.28923310105173655,1.0
0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.06283432838579693,0.0
0.4999999999999999,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.29536607456424097,0.5
0.4999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.2693258904641319,1.0
0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.10460804791038984,0.5
0.4999999999999999,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.4651485185116726,0.5"><pre class="notranslate"><code class="notranslate">critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price
0.4999999999999999,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.3747139609249367,1.0
0.8333333333333334,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.19242527864549636,0.5
0.33333333333333337,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.11485185116726125,0.5
0.8333333333333334,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.14245208036820023,0.0
0.6666666666666666,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.4806824273118796,1.0
0.6666666666666666,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.13972015304707863,0.0
0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.11338792258923126,0.5
0.8333333333333334,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.44906748488937354,1.0
0.4999999999999999,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.06127428328496702,0.0
0.16666666666666663,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.20668009833459638,1.0
0.8333333333333334,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.4777545701558197,1.0
0.4999999999999999,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.13232657437015954,1.0
0.4999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.17925361823256503,0.5
0.6666666666666666,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.16335742407718895,1.0
0.4999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.23946692297739414,1.0
0.6666666666666666,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.31206816879540117,1.0
0.4999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.2285281233248923,0.5
0.33333333333333337,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.29274505092327313,1.0
0.16666666666666663,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.3480564130053049,0.5
0.9999999999999999,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.42799208887081575,1.0
0.6666666666666666,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.2248313339864328,1.0
0.8333333333333334,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.1280235115801926,0.5
0.16666666666666663,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.40164507125561455,1.0
0.6666666666666666,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.2963420269495943,0.5
0.8333333333333334,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.2486090830114046,0.0
0.8333333333333334,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.28923310105173655,1.0
0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.06283432838579693,0.0
0.4999999999999999,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.29536607456424097,0.5
0.4999999999999999,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.2693258904641319,1.0
0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.10460804791038984,0.5
0.4999999999999999,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.4651485185116726,0.5
</code></pre></div>