title: How to apply a trained neural network to write outputs into a csv file?
body: <p>I have the following dataset,<code>data_num</code>, on which I have trained the neural network, here is sample of <code>data_num</code>:</p>
  
  <pre><code>    A   B   C   D  Label1
  0  95  91   3  10       9
  1  91  95   4   3       9
  2  68  65  31  31       6
  3  50  43  51  58       4
  4   8   4  93  97       0
  5  16  20  81  90       1
  6  75  79  28  26       7
  7  74  76  27  22       7
  8  45  46  56  57       4
  9   5   7  97  93       0
  </code></pre>
  
  <p>Here's the entire code:</p>
  
  <pre><code>import keras
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Activation
  from keras.optimizers import SGD
  import pandas as pd
  import numpy as np
  import matplotlib
  from matplotlib import style
  import matplotlib.pyplot as plt
  from sklearn.model_selection import train_test_split
  from IPython.core.display import display
  from sklearn.preprocessing import MinMaxScaler
  
  matplotlib.style.use('ggplot')
  
  data_num = pd.read_csv('mult_test.csv')
  
  scaler = MinMaxScaler(feature_range=(0, 1))
  features = data_num.drop(['Label1'], axis=1, errors='ignore')
  features = pd.DataFrame(scaler.fit_transform(features))
  scale_num_data = pd.concat([data_num['Label1'], features], axis=1)
  
  
  dtrain, dtest = train_test_split(scale_num_data, test_size=0.25, random_state=570)
  X = dtrain.drop(['Label1'], axis=1, errors='ignore')
  y = dtrain['Label1']
  Xtest = dtest.drop(['Label1'], axis=1, errors='ignore')
  ytest = dtest['Label1']
  
  
  model = Sequential([
      Dense(10, input_shape=(4, ), activation='relu'),
      Dense(32, activation='relu'),
      Dense(10, activation='softmax')
  ])
  
  model.summary()
  model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  model.fit(X, y, epochs=10, batch_size=10, shuffle=True)
  
  
  scores = model.evaluate(Xtest, ytest, batch_size=5)
  print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
  </code></pre>
  
  <p>Now that the network is trained, I would like to apply the code to a new dataset, <code>predict_num</code> and print the NN output into a new column 'Predictions'. Here's a sample of the new dataset:</p>
  
  <pre><code>  Stock   A   B    C   D  Prediction
  0  AMCX  46  43   57  52           
  1  ABAC  83  86   11  18           
  2  AKAM  55  52   45  43           
  3   ACW  96  99    9   8           
  4  AOLS  46  43   54  52           
  5  ABAX   9   9  100  95           
  6  AMTX   9   1   91  97           
  7  ABMC  73  79   29  25           
  8   ALE  58  56   50  44           
  9  AMAT   8   1   98  92           
  </code></pre>
  
  <p><code>predict_num</code> also has an extra column 'Stock', so I would like to specify the features only as columns [A, B, C, D] and fill the column 'Predictions' with the output of the NN.</p>
  
  <p>The final dataset should look like this:</p>
  
  <pre><code>  Stock   A   B    C   D  Prediction
  0  AMCX  46  43   57  52           4
  1  ABAC  83  86   11  18           8
  2  AKAM  55  52   45  43           5
  3   ACW  96  99    9   8           9
  4  AOLS  46  43   54  52           4
  5  ABAX   9   9  100  95           0
  6  AMTX   9   1   91  97           0
  7  ABMC  73  79   29  25           7
  8   ALE  58  56   50  44           5
  9  AMAT   8   1   98  92           0
  </code></pre>
  
  <p>Thank you so much for your help.</p>