 `hidden_layer_sizes=(7,)` if you want only 1 hidden layer with 7 hidden units.

`length = n_layers - 2` is because you have 1 input layer and 1 output layer.



In the [docs][1]: 
&gt;hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)

 means :
hidden_layer_sizes is a tuple of size (n_layers -2)

n_layers means no of layers we want as per architecture.

Value 2 is subtracted from n_layers because two layers (input &amp; output ) are not part of hidden layers, so not belong to the count.

default(100,) means if no value is provided for hidden_layer_sizes then default architecture will have one input layer, one hidden layer with 100 units and one output layer.

From the docs again:
&gt;The ith element represents the number of neurons in the ith hidden layer.

 means each entry in tuple belongs to corresponding hidden layer.

Example :

1. For architecture 56:25:11:7:5:3:1 with input 56 and 1 output
hidden layers will be (25:11:7:5:3). So tuple `hidden_layer_sizes = (25,11,7,5,3,)` 

2. For architecture 3:45:2:11:2 with input 3 and 2 output
hidden layers will be (45:2:11). So tuple `hidden_layer_sizes = (45,2,11,)` 




  [1]: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html

I am making a React w/ Webpack setup and am struggling to do what seems like should be a simple task. I want webpack to include images, and minimize them like I with gulp but I can&#39;t figure it out. I just want to be able to link an image in my css like so:
    
    /* ./src/img/background.jpg */

    body { background: url(&#39;./img/background.jpg&#39;); }

I have all of my css/js/img folders inside a src folder. Webpack outputs to a dist folder, but I can&#39;t figure out how to get images there.

Here is my webpack setup:

     var path = require(&#39;path&#39;);
     var webpack = require(&#39;webpack&#39;);
     var HtmlWebpackPlugin = require(&#39;html-webpack-plugin&#39;);

     module.exports = {
      devtool: &#39;cheap-eval-source-map&#39;,
      entry: [
       &#39;webpack-dev-server/client?http://localhost:8080&#39;,
       &#39;webpack/hot/dev-server&#39;,
       &#39;./src/index.js&#39;
      ],
      output: {
      path: path.join(__dirname, &#39;dist&#39;),
      //  publicPath: &#39;./dist&#39;,
      filename: &#39;bundle.js&#39;
      },
      plugins: [
      new webpack.HotModuleReplacementPlugin(),
      new HtmlWebpackPlugin({
      template: &#39;./src/index.html&#39;
       })
      ],
      module: {
      loaders: [{
       exclude: /node_modules/,
       test: /\.js?$/,
       loader: &#39;babel&#39;
       }, {
      test: /\.scss$/,
      loader: &#39;style!css!sass&#39;
        }, {
      test: /\.(png|jpg)$/,
      loader: &#39;file-loader&#39;
      }]
     },

     devServer: {
      historyApiFallback: true,
      contentBase: &#39;./dist&#39;,
      hot: true
      }
    };




How to use images in css with Webpack

Is there a way to check playbook syntax and variables?

I&#39;m trying to dry-run(--check) but for some reasons it works really slow. It looks like it tries to perform an action instead of just check the syntax

I want to omit en errors like this:

    ...&quot;msg&quot;: &quot;AnsibleUndefinedVariable: ERROR! &#39;application_name&#39; is undefined&quot;}

Ansible. Fast way to check syntax?



I am lost in the scikit learn 0.18 user manual (http://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier):

       hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
       The ith element represents the number of neurons in the ith hidden layer.

If I am looking for only 1 hidden layer and 7 hidden units in my model, should I put like this?  Thanks!

        hidden_layer_sizes=(7, 1)



Python scikit learn MLPClassifier &quot;hidden_layer_sizes&quot;

I am lost in the scikit learn 0.18 user manual (<a href="http://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier" target="_blank" rel="noopener noreferrer">http://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier</a>):
<pre><code class="hljs language-ini"> hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
 The ith element represents the number of neurons in the ith hidden layer.
</code></pre>
If I am looking for only 1 hidden layer and 7 hidden units in my model, should I put like this? Thanks!
<pre><code class="hljs language-ini"> hidden_layer_sizes=(7, 1)
</code></pre>

I want to change a dataframes&#39; index (rows) from float64 to string or unicode. 

I thought this would work but apparently not:

    #check type
    type(df.index)
    &#39;pandas.core.index.Float64Index&#39;

    #change type to unicode
    if not isinstance(df.index, unicode):
        df.index = df.index.astype(unicode)

error message:

    TypeError: Setting &lt;class &#39;pandas.core.index.Float64Index&#39;&gt; dtype to anything other than float64 or object is not supported

pandas - change df.index from float64 to unicode or string

Here is my code: 

    import csv
    import requests
    with requests.Session() as s:
        s.post(url, data=payload)
        download = s.get(&#39;url that directly download a csv report&#39;)

This gives me the access to the csv file. I tried different method to deal with the download:

This will give the the csv file in one string:

    print download.content

This print the first row and return error: _csv.Error: new-line character seen in unquoted field

    cr = csv.reader(download, dialect=csv.excel_tab)
    for row in cr:
        print row

This will print a letter in each row and it won&#39;t print the whole thing:

    cr = csv.reader(download.content, dialect=csv.excel_tab)
    for row in cr:
        print row

My question is: what&#39;s the most efficient way to read a csv file in this situation. 
And how to download it.

thanks 


Use python requests to download CSV

I&#39;m trying understand what 0xFF does under the hood in the following code snippet:

	if cv2.waitKey(0) &amp; 0xFF == ord(&#39;q&#39;):
		break

Any ideas?

What&#39;s 0xFF for in cv2.waitKey(1)?

I&#39;m working with pandas and need to read some csv files, the structure is something like this:

&gt;folder/folder2/scripts_folder/script.py
&gt;
&gt;folder/folder2/data_folder/data.csv

How can I open the `data.csv` file from the script in `scripts_folder`?


I&#39;ve tried this:


    absolute_path = os.path.abspath(os.path.dirname(&#39;data.csv&#39;))

    pandas.read_csv(absolute_path + &#39;/data.csv&#39;)

I get this error:

    File folder/folder2/data_folder/data.csv does not exist






How to open my files in data_folder with pandas using relative path?

I am new to python and couldn&#39;t find the answer to this. Referring to the code at the end of the message, can I know what does the part &quot;for item, total in totals.items()&quot; in the line below mean?

    rankings = [(total/simSums[item], item) for item, total in totals.items()]

Also, the code failed and said

&gt; AttributeError: &#39;dict&#39; object has no attribute &#39;predictors&#39;

when I changed all instances of &quot;item(s)&quot; in the code to &quot;predictor(s)&quot;. Why is that so?

    # Return the Pearson correlation coefficient for p1 and p2
    def sim_person(prefs, p1, p2):
        # Get the list of shared_items
        si={}
        for item in prefs[p1]:
            if item in prefs[p2]:si[item]=1
    
        # Find the number of elements 
        n=len(si)
    
        # if they have no ratings in common, return 0
        if n==0: return 0
    
        # Add up all the preferences
        sum1 = sum([prefs[p1][it] for it in si])
        sum2 = sum([prefs[p2][it] for it in si])
    
        # Sum up the squares
        sum1Sq = sum([pow(prefs[p1][it],2) for it in si])
        sum2Sq = sum([pow(prefs[p2][it],2) for it in si])
    
        # Sum up the products
        pSum = sum([prefs[p1][it]*prefs[p2][it] for it in si])
    
        # Calculate Person score
        num = pSum - (sum1*sum2/n)
        den = sqrt((sum1Sq - pow(sum1,2)/n)*(sum2Sq - pow(sum2,2)/n))
        if den == 0: return 0
    
        r = num/den
        return r
    
    # Returns the best matches for person from the prefs dictionary.
    # Number of results and similarity function are optional params.
    def topMatch(prefs, person, n=5, similarity=sim_person):
        scores = [(similarity(prefs, person, other), other) 
                  for other in prefs if other!=person]
    
        # Sort the list so the highest scores appear at the top
        scores.sort()
        scores.reverse()
        return scores[0:n]
    
    # Gets recommendations for a person by using a weighted average
    # of every other user&#39;s rankings 
    def getRecommendations(prefs, person, similarity=sim_person):
        totals = {}
        simSums = {}
        for other in prefs:
            # don&#39;t compare me to myself
            if other == person: continue
            sim = similarity(prefs, person, other)
    
            # ignore scores of zero of lower
            if sim&lt;=0: continue
            for item in prefs[other]:
    
                # only score movies I haven&#39;t seen yet
                if item not in prefs[person] or prefs[person][item]==0:
                    # Similarity * Score
                    totals.setdefault(item, 0)
                    totals[item]+=prefs[other][item]*sim
                    # Sum of similarities
                    simSums.setdefault(item, 0)
                    simSums[item]+=sim
    
        # Create the normalized list 
        rankings = [(total/simSums[item], item) for item, total in totals.items()]
    
        # Return the sorted list 
        rankings.sort()
        rankings.reverse()
        return rankings


AttributeError: &#39;dict&#39; object has no attribute &#39;predictors&#39;

I&#39;d like to create a class which has `abc.ABCMeta` as a metaclass and is compatible both with Python 2.7 and Python 3.5. Until now, I only succeeded doing this either on 2.7 or on 3.5 - but never on both versions simultaneously. Could someone give me a hand?

Python 2.7:

    import abc
    class SomeAbstractClass(object):
        __metaclass__ = abc.ABCMeta
        @abc.abstractmethod
        def do_something(self):
            pass

Python 3.5:

    import abc
    class SomeAbstractClass(metaclass=abc.ABCMeta):
        @abc.abstractmethod
        def do_something(self):
            pass

## Testing

If we run the following test using the suitable version of the Python interpreter (Python 2.7 -&gt; Example 1, Python 3.5 -&gt; Example 2), it succeeds in both scenarios:

    import unittest
    class SomeAbstractClassTestCase(unittest.TestCase):
        def test_do_something_raises_exception(self):
            with self.assertRaises(TypeError) as error:
                processor = SomeAbstractClass()
            msg = str(error.exception)
            expected_msg = &quot;Can&#39;t instantiate abstract class SomeAbstractClass with abstract methods do_something&quot;
            self.assertEqual(msg, expected_msg)

## Problem

While running the test using Python 3.5, the expected behavior doesn&#39;t happen (`TypeError` is not raised while instantiating `SomeAbstractClass`):

    ======================================================================
    FAIL: test_do_something_raises_exception (__main__.SomeAbstractClassTestCase)
    ----------------------------------------------------------------------
    Traceback (most recent call last):
      File &quot;/home/tati/sample_abc.py&quot;, line 22, in test_do_something_raises_exception
        processor = SomeAbstractClass()
    AssertionError: TypeError not raised
    
    ----------------------------------------------------------------------

Whereas running the test using Python 2.7 raises a `SyntaxError`:

     Python 2.7 incompatible
     Raises exception:
      File &quot;/home/tati/sample_abc.py&quot;, line 24
        class SomeAbstractClass(metaclass=abc.ABCMeta):
                                         ^
     SyntaxError: invalid syntax

Using abc.ABCMeta in a way it is compatible both with Python 2.7 and Python 3.5

I am trying to understand what the best practices are with regards to Python&#39;s (v2.7) import mechanics. I have a project that has started to grow a bit and lets say my code is organised as follows:
    
    foo/
        __init__.py
        Foo.py
        module1.py
        module2.py
        module3.py

The package name is `foo` and underneath it I have module `Foo.py` which contains code for the class `Foo`. Hence I am using the same name for the package, module and class which might not be very clever to start with.

`__init__.py` is empty and class `Foo` needs to import `module1, module2 and module3` hence part of my `Foo.py` file looks like:
    
    # foo/Foo.py
    
    import module1
    import module2
    import module3

    class Foo(object):
        def __init__(self):
    ....
    ....
    if __name__ == &#39;__main__&#39;:
        foo_obj = Foo()

However I later revisited this and I thought it would be better to have all imports in the `__init__.py` file. Hence my `__init__.py` now looks like:
    
    # foo/__init__.py
    
    import Foo
    import module1
    import module2
    import module3
    ....
    ....
        
and my `Foo.py` only needs to import `foo`:

    # foo/Foo.py
    
    import foo

While this looks convenient since it is an one liner, I am a bit worried that it might be creating circular imports. What I mean is that when the script `Foo.py` is run it will import everything it can and then `__init__.py` will be called which will import `Foo.py` again (is that correct?). Additionally using the same name for package, module and class makes things more confusing.

Does it make sense the way I have done it? Or am I asking for trouble?

Module imports and __init__.py

After I installed Python and Djangom, I&#39;m trying to use `virtualenv` for django project purpose using virtualenv. I installed virtualenv using pip.

    pip install virtualenv # got install successfully

When I tried to run it, I got the error message 
```
C:\Users\gshiv\Desktop\DjangoProject&gt;virtualenv
&#39;virtualenv&#39; is not recognized as an internal or external command,
operable program or batch file.
```

&#39;virtualenv&#39; is not recognized as an internal or external command, operable program or batch file

As a complete beginner to programming, I am trying to understand the basic concepts of opening and closing files. One exercise I am doing is creating a script that allows me to copy the contents from one file to another. 


    in_file = open(from_file)
    indata = in_file.read()
    
    out_file = open(to_file, &#39;w&#39;)
    out_file.write(indata)
    
    out_file.close()
    in_file.close()


I have tried to shorten this code and came up with this:


    indata = open(from_file).read()
    open(to_file, &#39;w&#39;).write(indata)


This works and looks a bit more efficient to me. However, this is also where I get confused. I think I left out the references to the opened files; there was no need for the in_file and out_file variables. However, does this leave me with two files that are open, but have nothing referring to them? How do I close these, or is there no need to?

Any help that sheds some light on this topic is much appreciated.

Is there a need to close files that have no reference to them?

I searched in [this][1] official document to find difference between the json.dump() and json.dumps() in python. It is clear that they are related with file write option. &lt;br&gt;But what is the detailed difference between them and in what situations one has more advantage than other?


  [1]: https://docs.python.org/2/library/json.html

What is the difference between json.dump() and json.dumps() in python?

I am using scikit-learn for classification of text documents(22000) to 100 classes. I use scikit-learn&#39;s confusion matrix method for computing the confusion matrix.

    model1 = LogisticRegression()
    model1 = model1.fit(matrix, labels)
    pred = model1.predict(test_matrix)
    cm=metrics.confusion_matrix(test_labels,pred)
    print(cm)
    plt.imshow(cm, cmap=&#39;binary&#39;)
This is how my confusion matrix looks like:

    [[3962  325    0 ...,    0    0    0]
     [ 250 2765    0 ...,    0    0    0]
     [   2    8   17 ...,    0    0    0]
     ..., 
     [   1    6    0 ...,    5    0    0]
     [   1    1    0 ...,    0    0    0]
     [   9    0    0 ...,    0    0    9]]

However, I do not receive a clear or legible plot. Is there a better way to do this?

How can I plot a confusion matrix?

I want to apply scaling (using StandardScaler() from sklearn.preprocessing) to a pandas dataframe. The following code returns a numpy array, so I lose all the column names and indeces. This is not what I want.

    features = df[[&quot;col1&quot;, &quot;col2&quot;, &quot;col3&quot;, &quot;col4&quot;]]
    autoscaler = StandardScaler()
    features = autoscaler.fit_transform(features)

A &quot;solution&quot; I found online is:

    features = features.apply(lambda x: autoscaler.fit_transform(x))

It appears to work, but leads to a deprecationwarning:

&gt; /usr/lib/python3.5/site-packages/sklearn/preprocessing/data.py:583:
&gt; DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17
&gt; and will raise ValueError in 0.19. Reshape your data either using
&gt; X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1)
&gt; if it contains a single sample.

I therefore tried:

    features = features.apply(lambda x: autoscaler.fit_transform(x.reshape(-1, 1)))

But this gives:

&gt; Traceback (most recent call last):   File &quot;./analyse.py&quot;, line 91, in
&gt; &lt;module&gt;
&gt;     features = features.apply(lambda x: autoscaler.fit_transform(x.reshape(-1, 1)))   File
&gt; &quot;/usr/lib/python3.5/site-packages/pandas/core/frame.py&quot;, line 3972, in
&gt; apply
&gt;     return self._apply_standard(f, axis, reduce=reduce)   File &quot;/usr/lib/python3.5/site-packages/pandas/core/frame.py&quot;, line 4081, in
&gt; _apply_standard
&gt;     result = self._constructor(data=results, index=index)   File &quot;/usr/lib/python3.5/site-packages/pandas/core/frame.py&quot;, line 226, in
&gt; __init__
&gt;     mgr = self._init_dict(data, index, columns, dtype=dtype)   File &quot;/usr/lib/python3.5/site-packages/pandas/core/frame.py&quot;, line 363, in
&gt; _init_dict
&gt;     dtype=dtype)   File &quot;/usr/lib/python3.5/site-packages/pandas/core/frame.py&quot;, line 5163, in
&gt; _arrays_to_mgr
&gt;     arrays = _homogenize(arrays, index, dtype)   File &quot;/usr/lib/python3.5/site-packages/pandas/core/frame.py&quot;, line 5477, in
&gt; _homogenize
&gt;     raise_cast_failure=False)   File &quot;/usr/lib/python3.5/site-packages/pandas/core/series.py&quot;, line 2885,
&gt; in _sanitize_array
&gt;     raise Exception(&#39;Data must be 1-dimensional&#39;) Exception: Data must be 1-dimensional

How do I apply scaling to the pandas dataframe, leaving the dataframe intact? Without copying the data if possible.

How to use sklearn fit_transform with pandas and return dataframe instead of numpy array?

I&#39;m learning different methods to convert categorical variables to numeric for machine-learning classifiers.  I came across the `pd.get_dummies` method and `sklearn.preprocessing.OneHotEncoder()` and I wanted to see how they differed in terms of performance and usage. 

I found a tutorial on how to use `OneHotEncoder()` on https://xgdgsc.wordpress.com/2015/03/20/note-on-using-onehotencoder-in-scikit-learn-to-work-on-categorical-features/ since the `sklearn` documentation wasn&#39;t too helpful on this feature. I have a feeling I&#39;m not doing it correctly...but

**Can some explain the pros and cons of using `pd.dummies` over `sklearn.preprocessing.OneHotEncoder()` and vice versa?** I know that `OneHotEncoder()` gives you a sparse matrix but other than that I&#39;m not sure how it is used and what the benefits are over the `pandas` method.  Am I using it inefficiently? 

    import pandas as pd
    import numpy as np
    from sklearn.datasets import load_iris
    sns.set()
    
    %matplotlib inline
    
    #Iris Plot
    iris = load_iris()
    n_samples, m_features = iris.data.shape
    
    #Load Data
    X, y = iris.data, iris.target
    D_target_dummy = dict(zip(np.arange(iris.target_names.shape[0]), iris.target_names))
    
    DF_data = pd.DataFrame(X,columns=iris.feature_names)
    DF_data[&quot;target&quot;] = pd.Series(y).map(D_target_dummy)
    #sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \
    #0                  5.1               3.5                1.4               0.2   
    #1                  4.9               3.0                1.4               0.2   
    #2                  4.7               3.2                1.3               0.2   
    #3                  4.6               3.1                1.5               0.2   
    #4                  5.0               3.6                1.4               0.2   
    #5                  5.4               3.9                1.7               0.4   
    
    DF_dummies = pd.get_dummies(DF_data[&quot;target&quot;])
    #setosa  versicolor  virginica
    #0         1           0          0
    #1         1           0          0
    #2         1           0          0
    #3         1           0          0
    #4         1           0          0
    #5         1           0          0
    
    from sklearn.preprocessing import OneHotEncoder, LabelEncoder
    def f1(DF_data):
    	Enc_ohe, Enc_label = OneHotEncoder(), LabelEncoder()
    	DF_data[&quot;Dummies&quot;] = Enc_label.fit_transform(DF_data[&quot;target&quot;])
    	DF_dummies2 = pd.DataFrame(Enc_ohe.fit_transform(DF_data[[&quot;Dummies&quot;]]).todense(), columns = Enc_label.classes_)
    	return(DF_dummies2)
    	
    %timeit pd.get_dummies(DF_data[&quot;target&quot;])
    #1000 loops, best of 3: 777 &#181;s per loop
    
    %timeit f1(DF_data)
    #100 loops, best of 3: 2.91 ms per loop




What are the pros and cons between get_dummies (Pandas) and OneHotEncoder (Scikit-learn)?

I&#39;m using `linear_model.LinearRegression` from scikit-learn as a predictive model. It works and it&#39;s perfect. I have a problem to evaluate the predicted results using the `accuracy_score` metric.

This is my true Data :

    array([1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0])

**My predicted Data:**

    array([ 0.07094605,  0.1994941 ,  0.19270157,  0.13379635,  0.04654469,
        0.09212494,  0.19952108,  0.12884365,  0.15685076, -0.01274453,
        0.32167554,  0.32167554, -0.10023553,  0.09819648, -0.06755516,
        0.25390082,  0.17248324])

**My code:**

    accuracy_score(y_true, y_pred, normalize=False)

**Error message:** 

    ValueError: Can&#39;t handle mix of binary and continuous target



Accuracy Score ValueError: Can&#39;t Handle mix of binary and continuous target

How do I convert data from a Scikit-learn Bunch object to a Pandas DataFrame?

    from sklearn.datasets import load_iris
    import pandas as pd
    data = load_iris()
    print(type(data))
    data1 = pd. # Is there a Pandas method to accomplish this?


How to convert a Scikit-learn dataset to a Pandas dataset

After training a word2vec model using python [gensim](http://radimrehurek.com/gensim/models/word2vec.html), how do you find the number of words in the model&#39;s vocabulary?

gensim word2vec: Find number of words in vocabulary

I am currently seeing the API of theano,

    theano.tensor.nnet.conv2d(input, filters, input_shape=None, filter_shape=None, border_mode=&#39;valid&#39;, subsample=(1, 1), filter_flip=True, image_shape=None, **kwargs)

where the `filter_shape` is a tuple of `(num_filter, num_channel, height, width)`, I am confusing about this because isn&#39;t that the number of filter decided by the stride while sliding the filter window on the image? How can I specify on filter number just like this? It would be reasonable to me if it is calculated by the parameter stride (if there is any).

Also, I am confused with the term feature map as well, is it the neurons at each layer? How about the batch size? How are they correlated?



What is the number of filter in CNN?

Although both of the above methods provide a better score for the better closeness of prediction, still cross-entropy is preferred. Is it in every case or there are some peculiar scenarios where we prefer cross-entropy over MSE? 

In which cases is the cross-entropy preferred over the mean squared error?

I have a simple NN model for detecting hand-written digits from a 28x28px image written in python using Keras (Theano backend):

    model0 = Sequential()

    #number of epochs to train for
    nb_epoch = 12
    #amount of data each iteration in an epoch sees
    batch_size = 128

    model0.add(Flatten(input_shape=(1, img_rows, img_cols)))
    model0.add(Dense(nb_classes))
    model0.add(Activation(&#39;softmax&#39;))
    model0.compile(loss=&#39;categorical_crossentropy&#39;, 
             optimizer=&#39;sgd&#39;,
             metrics=[&#39;accuracy&#39;])

    model0.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
          verbose=1, validation_data=(X_test, Y_test))

    score = model0.evaluate(X_test, Y_test, verbose=0)

    print(&#39;Test score:&#39;, score[0])
    print(&#39;Test accuracy:&#39;, score[1])

This runs well and I get ~90% accuracy. I then perform the following command to get a summary of my network&#39;s structure by doing `print(model0.summary())`. This outputs the following:

    Layer (type)         Output Shape   Param #     Connected to                     
    =====================================================================
    flatten_1 (Flatten)   (None, 784)     0           flatten_input_1[0][0]            
    dense_1 (Dense)     (None, 10)       7850        flatten_1[0][0]                  
    activation_1        (None, 10)          0           dense_1[0][0]                    
    ======================================================================
    Total params: 7850

I don&#39;t understand how they get to 7850 total params and what that actually means?

Keras model.summary() result - Understanding the # of Parameters

Using Anaconda Python 2.7 Windows 10.

I am training a language model using the Keras exmaple:

    print(&#39;Build model...&#39;)
    model = Sequential()
    model.add(GRU(512, return_sequences=True, input_shape=(maxlen, len(chars))))
    model.add(Dropout(0.2))
    model.add(GRU(512, return_sequences=False))
    model.add(Dropout(0.2))
    model.add(Dense(len(chars)))
    model.add(Activation(&#39;softmax&#39;))

    model.compile(loss=&#39;categorical_crossentropy&#39;, optimizer=&#39;rmsprop&#39;)

    def sample(a, temperature=1.0):
        # helper function to sample an index from a probability array
        a = np.log(a) / temperature
        a = np.exp(a) / np.sum(np.exp(a))
        return np.argmax(np.random.multinomial(1, a, 1))


    # train the model, output generated text after each iteration
    for iteration in range(1, 3):
        print()
        print(&#39;-&#39; * 50)
        print(&#39;Iteration&#39;, iteration)
        model.fit(X, y, batch_size=128, nb_epoch=1)
        start_index = random.randint(0, len(text) - maxlen - 1)

        for diversity in [0.2, 0.5, 1.0, 1.2]:
            print()
            print(&#39;----- diversity:&#39;, diversity)

            generated = &#39;&#39;
            sentence = text[start_index: start_index + maxlen]
            generated += sentence
            print(&#39;----- Generating with seed: &quot;&#39; + sentence + &#39;&quot;&#39;)
            sys.stdout.write(generated)

            for i in range(400):
                x = np.zeros((1, maxlen, len(chars)))
                for t, char in enumerate(sentence):
                    x[0, t, char_indices[char]] = 1.

                preds = model.predict(x, verbose=0)[0]
                next_index = sample(preds, diversity)
                next_char = indices_char[next_index]

                generated += next_char
                sentence = sentence[1:] + next_char

                sys.stdout.write(next_char)
                sys.stdout.flush()
            print()

According to Keras documentation, the `model.fit` method returns a History callback, which has a history attribute containing the lists of successive losses and other metrics.

    hist = model.fit(X, y, validation_split=0.2)
    print(hist.history)

After training my model, if I run `print(model.history)` I get the error:

     AttributeError: &#39;Sequential&#39; object has no attribute &#39;history&#39;

How do I return my model history after training my model with the above code?


**UPDATE**

The issue was that:

The following had to first be defined:

    from keras.callbacks import History 
    history = History()

The callbacks option had to be called
    
    model.fit(X_train, Y_train, nb_epoch=5, batch_size=16, callbacks=[history])

But now if I print

    print(history.History)

it returns

    {}

even though I ran an iteration. 

Content Type	Original Author	Original Content on Stackoverflow
Question	Chubaka	View Question on Stackoverflow
Solution 1 - Python	Farseer	View Answer on Stackoverflow
Solution 2 - Python	shantanu pathak	View Answer on Stackoverflow

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