import tensorflow as tf

from sklearn import preprocessing
from sklearn.model_selection import train_test_split

from old.matt.utils.helper import get_training_data


class ANN:
    def __init__(self, training_set, training_anno_file, test_set):
        self.training_set = training_set
        self.training_anno_file = training_anno_file
        self.test_set = test_set

    def prep_training_data(self):
        # Get training data in np.array format
        X_train, y_train, class_label_pair, X_train_ids = get_training_data(self.training_set, self.training_anno_file)

        # Split validation set from training data
        X_train, X_test, y_train, y_test = train_test_split(X_train, y_train,
                                                            test_size=0.2,
                                                            random_state=1,
                                                            stratify=y_train)

        # Preprocess the data
        scaler = preprocessing.StandardScaler()
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)

        return X_train_scaled, X_test_scaled, y_train, y_test

    def train_model(self):
        X_train, X_test, y_train, y_test = self.prep_training_data()

        # Create ANN classifier
        model = tf.keras.models.Sequential()
        model.add(tf.keras.layers.Flatten())
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(1, activation='sigmoid'))  # Probability distribution

        model.compile(optimizer='adam',
                      loss='binary_crossentropy',  # Tries to minimize loss
                      metrics=['accuracy'])

        model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1)

        return model

    def load_saved_model(self, loaded_model):
        X_train, X_test, y_train, y_test = self.prep_training_data()

        loaded_model.compile(optimizer='adam',
                      loss='binary_crossentropy',  # Tries to minimize loss
                      metrics=['accuracy'])

        score = loaded_model.evaluate(X_test, y_test, verbose=0)

        print('%s: %.2f%%' % (loaded_model.metrics_names[1], score[1]*100))

        return loaded_model

1230121360342f29858b4695d7cd347be94310af
<mask token>


class ANN:
    <mask token>

    def prep_training_data(self):
        X_train, y_train, class_label_pair, X_train_ids = get_training_data(
            self.training_set, self.training_anno_file)
        X_train, X_test, y_train, y_test = train_test_split(X_train,
            y_train, test_size=0.2, random_state=1, stratify=y_train)
        scaler = preprocessing.StandardScaler()
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)
        return X_train_scaled, X_test_scaled, y_train, y_test
    <mask token>
    <mask token>

<mask token>


class ANN:

    def __init__(self, training_set, training_anno_file, test_set):
        self.training_set = training_set
        self.training_anno_file = training_anno_file
        self.test_set = test_set

    def prep_training_data(self):
        X_train, y_train, class_label_pair, X_train_ids = get_training_data(
            self.training_set, self.training_anno_file)
        X_train, X_test, y_train, y_test = train_test_split(X_train,
            y_train, test_size=0.2, random_state=1, stratify=y_train)
        scaler = preprocessing.StandardScaler()
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)
        return X_train_scaled, X_test_scaled, y_train, y_test

    def train_model(self):
        X_train, X_test, y_train, y_test = self.prep_training_data()
        model = tf.keras.models.Sequential()
        model.add(tf.keras.layers.Flatten())
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
        model.compile(optimizer='adam', loss='binary_crossentropy', metrics
            =['accuracy'])
        model.fit(X_train, y_train, epochs=50, batch_size=32,
            validation_split=0.1)
        return model
    <mask token>

<mask token>


class ANN:

    def __init__(self, training_set, training_anno_file, test_set):
        self.training_set = training_set
        self.training_anno_file = training_anno_file
        self.test_set = test_set

    def prep_training_data(self):
        X_train, y_train, class_label_pair, X_train_ids = get_training_data(
            self.training_set, self.training_anno_file)
        X_train, X_test, y_train, y_test = train_test_split(X_train,
            y_train, test_size=0.2, random_state=1, stratify=y_train)
        scaler = preprocessing.StandardScaler()
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)
        return X_train_scaled, X_test_scaled, y_train, y_test

    def train_model(self):
        X_train, X_test, y_train, y_test = self.prep_training_data()
        model = tf.keras.models.Sequential()
        model.add(tf.keras.layers.Flatten())
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
        model.compile(optimizer='adam', loss='binary_crossentropy', metrics
            =['accuracy'])
        model.fit(X_train, y_train, epochs=50, batch_size=32,
            validation_split=0.1)
        return model

    def load_saved_model(self, loaded_model):
        X_train, X_test, y_train, y_test = self.prep_training_data()
        loaded_model.compile(optimizer='adam', loss='binary_crossentropy',
            metrics=['accuracy'])
        score = loaded_model.evaluate(X_test, y_test, verbose=0)
        print('%s: %.2f%%' % (loaded_model.metrics_names[1], score[1] * 100))
        return loaded_model

import tensorflow as tf
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from old.matt.utils.helper import get_training_data


class ANN:

    def __init__(self, training_set, training_anno_file, test_set):
        self.training_set = training_set
        self.training_anno_file = training_anno_file
        self.test_set = test_set

    def prep_training_data(self):
        X_train, y_train, class_label_pair, X_train_ids = get_training_data(
            self.training_set, self.training_anno_file)
        X_train, X_test, y_train, y_test = train_test_split(X_train,
            y_train, test_size=0.2, random_state=1, stratify=y_train)
        scaler = preprocessing.StandardScaler()
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)
        return X_train_scaled, X_test_scaled, y_train, y_test

    def train_model(self):
        X_train, X_test, y_train, y_test = self.prep_training_data()
        model = tf.keras.models.Sequential()
        model.add(tf.keras.layers.Flatten())
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
        model.compile(optimizer='adam', loss='binary_crossentropy', metrics
            =['accuracy'])
        model.fit(X_train, y_train, epochs=50, batch_size=32,
            validation_split=0.1)
        return model

    def load_saved_model(self, loaded_model):
        X_train, X_test, y_train, y_test = self.prep_training_data()
        loaded_model.compile(optimizer='adam', loss='binary_crossentropy',
            metrics=['accuracy'])
        score = loaded_model.evaluate(X_test, y_test, verbose=0)
        print('%s: %.2f%%' % (loaded_model.metrics_names[1], score[1] * 100))
        return loaded_model

import tensorflow as tf

from sklearn import preprocessing
from sklearn.model_selection import train_test_split

from old.matt.utils.helper import get_training_data


class ANN:
    def __init__(self, training_set, training_anno_file, test_set):
        self.training_set = training_set
        self.training_anno_file = training_anno_file
        self.test_set = test_set

    def prep_training_data(self):
        # Get training data in np.array format
        X_train, y_train, class_label_pair, X_train_ids = get_training_data(self.training_set, self.training_anno_file)

        # Split validation set from training data
        X_train, X_test, y_train, y_test = train_test_split(X_train, y_train,
                                                            test_size=0.2,
                                                            random_state=1,
                                                            stratify=y_train)

        # Preprocess the data
        scaler = preprocessing.StandardScaler()
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)

        return X_train_scaled, X_test_scaled, y_train, y_test

    def train_model(self):
        X_train, X_test, y_train, y_test = self.prep_training_data()

        # Create ANN classifier
        model = tf.keras.models.Sequential()
        model.add(tf.keras.layers.Flatten())
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
        model.add(tf.keras.layers.Dense(1, activation='sigmoid'))  # Probability distribution

        model.compile(optimizer='adam',
                      loss='binary_crossentropy',  # Tries to minimize loss
                      metrics=['accuracy'])

        model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.1)

        return model

    def load_saved_model(self, loaded_model):
        X_train, X_test, y_train, y_test = self.prep_training_data()

        loaded_model.compile(optimizer='adam',
                      loss='binary_crossentropy',  # Tries to minimize loss
                      metrics=['accuracy'])

        score = loaded_model.evaluate(X_test, y_test, verbose=0)

        print('%s: %.2f%%' % (loaded_model.metrics_names[1], score[1]*100))

        return loaded_model
