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https://github.com/linsanity03/flower_prediction

Predict the species of flower by using pre-built classifier model from tensorflow
https://github.com/linsanity03/flower_prediction

keras machine-learning prediction-model tensorflow

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Predict the species of flower by using pre-built classifier model from tensorflow

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README 

        
# Prediction model for classifying flower



In this project we use pandas to collect data from a source and in-built tensorflow model to train the data.



To run this project,



- Create a virutal environment and activate the environment.

  `  virtualenv venv

\venv\Scripts\activate`



- Install the required dependencies.

  `pip install -r requirements.txt

`



- Run the **main.py** file.

  `python main.py`



**1. Data Collection:**

We get flower training and evaluation data from google drive links.



```

import tensorflow as tf

...



train_path = tf.keras.utils.get_file(

    "iris_training.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv")

test_path = tf.keras.utils.get_file(

    "iris_test.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv")



```



**2. Feature Extraction:**

Using the in-built feature column function of tensorflow, we get all the unique value from each column of the tf file.



```

# Feature columns describe how to use the input.

# We use the inbuilt function in tensorflow to get all the unique value represented in the data of certain features

my_feature_columns = []

for key in train.keys():

    my_feature_columns.append(tf.feature_column.numeric_column(key=key))

```



**3. Data Preparation:**

We need to make sure the data are in appropritate format for the tensorflow model. So, we convert the datas into data.Dataset object using tf.data.Dataset function



```

# convert the data into Dataset format

def input_fn(features, labels, training=True, batch_size=256):

    # Convert the inputs to a Dataset.

    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

    # Shuffle and repeat if you are in training mode.

    if training:

        dataset = dataset.shuffle(1000).repeat()



    return dataset.batch(batch_size)

```



**4. Choosing a Model:**

Our goal is to predict the flower based on given length. So, a DNN classifier model would do the trick.



```

# Creating a model

# Build a DNN with 2 hidden layers with 30 and 10 hidden nodes each.

classifier = tf.estimator.DNNClassifier(

    feature_columns=my_feature_columns,

    # Two hidden layers of 30 and 10 nodes respectively.

    hidden_units=[30, 10],

    # The model must choose between 3 classes.

    n_classes=3)

```



**5. Training the model:**

We use the data we convert to data.Dataset object to the model.



```

# training the model

classifier.train(

    input_fn=lambda: input_fn(train, train_y, training=True),

    steps=5000)

# We include a lambda to avoid creating an inner function previously

```



**6. Evaluate the model:**

Test the unseen dataset to measure the performance of the trained model.



```

# Evaluate the model accuracy by running the same data

eval_result = classifier.evaluate(

    input_fn=lambda: input_fn(test, test_y, training=False))



print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))

'''Test set accuracy: 0.900'''

```



**7. Make prediction:**

Using the evaluated model predict the flower species and return flower name with probability for better readability.



```

predictions = classifier.predict(input_fn=lambda: user_input(predict))

for pred_dict in predictions:

    class_id = pred_dict['class_ids'][0]

    probability = pred_dict['probabilities'][class_id]



    print('Prediction is "{}" ({:.1f}%)'.format(

        SPECIES[class_id], 100 * probability))



'''

Please type numeric values as prompted.

SepalLength: 5.1

SepalWidth: 5.9

PetalLength: 1.7

PetalWidth: 0.5

...

Prediction is "Setosa" (97.4%)

'''

```