import keras
import numpy as np
from keras.datasets import reuters
from keras.utils.np_utils import to_categorical
from keras import models
from keras import layers
import matplotlib.pyplot as plt

# keras.__version__


(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)
# len(train_data)
# len(test_data)
# train_data[10]
word_index = reuters.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
# Note that our indices were offset by 3
# because 0, 1 and 2 are reserved indices for "padding", "start of sequence", and "unknown".
decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])

# decoded_newswire
# train_labels[10]


def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results

# Our vectorized training data
x_train = vectorize_sequences(train_data)
# Our vectorized test data
x_test = vectorize_sequences(test_data)

def to_one_hot(labels, dimension=46):
    results = np.zeros((len(labels), dimension))
    for i, label in enumerate(labels):
        results[i, label] = 1.
    return results

# Our vectorized training labels
one_hot_train_labels = to_one_hot(train_labels)
# Our vectorized test labels
one_hot_test_labels = to_one_hot(test_labels)


one_hot_train_labels = to_categorical(train_labels)
one_hot_test_labels = to_categorical(test_labels)

def train(n_layers,n_unit,index):

    model = models.Sequential()
    model.add(layers.Dense(n_unit, activation='relu', input_shape=(10000,)))
    for _ in range(n_layers-1):
        model.add(layers.Dense(n_unit, activation='relu'))

    model.add(layers.Dense(46, activation='softmax'))

    model.compile(optimizer='rmsprop',
                loss='categorical_crossentropy',
                metrics=['accuracy'])
    x_val = x_train[:1000]
    partial_x_train = x_train[1000:]

    y_val = one_hot_train_labels[:1000]
    partial_y_train = one_hot_train_labels[1000:]
    history = model.fit(partial_x_train,
                        partial_y_train,
                        epochs=8,
                        batch_size=512,
                        validation_data=(x_val, y_val))
    results = model.evaluate(x_test, one_hot_test_labels)
    print(results)

    loss = history.history['loss']
    val_loss = history.history['val_loss']

    epochs = range(1, len(loss) + 1)
    plt.figure()
    plt.plot(epochs, loss, 'bo', label='Training loss')
    plt.plot(epochs, val_loss, 'b', label='Validation loss')
    plt.title('Training and validation loss')
    plt.xlabel('Epochs')
    plt.ylabel('Loss')
    plt.legend()

    plt.savefig('ex'+str(index)+'_loss.png')

    plt.figure()
    acc = history.history['acc']
    val_acc = history.history['val_acc']

    plt.plot(epochs, acc, 'bo', label='Training acc')
    plt.plot(epochs, val_acc, 'b', label='Validation acc')
    plt.title('Training and validation accuracy')
    plt.xlabel('Epochs')
    plt.ylabel('Acc')
    plt.legend()
    plt.savefig('ex'+str(index)+'_acc.png')

config = [(2,64),(1,64),(3,64),(2,32),(2,128)]

for i,(n_layers,n_unit) in enumerate(config):
    train(n_layers,n_unit,i)

# import copy

# test_labels_copy = copy.copy(test_labels)
# np.random.shuffle(test_labels_copy)
# float(np.sum(np.array(test_labels) == np.array(test_labels_copy))) / len(test_labels)


# predictions = model.predict(x_test)
# predictions[0].shape
# np.sum(predictions[0])
# np.argmax(predictions[0])

# y_train = np.array(train_labels)
# y_test = np.array(test_labels)
# model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['acc'])
