%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (7,7) # Make the figures a bit bigger

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.utils import np_utils

nb_classes = 10

# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print("X_train original shape", X_train.shape)
print("y_train original shape", y_train.shape)

for i in range(9):
    plt.subplot(3,3,i+1)
    plt.imshow(X_train[i], cmap='gray', interpolation='none')
    plt.title("Class {}".format(y_train[i]))

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print("Training matrix shape", X_train.shape)
print("Testing matrix shape", X_test.shape)

Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu')) 
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax')) 

model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['acc'])

model.fit(X_train, Y_train, batch_size = 128, epochs=4, validation_data=(X_test, Y_test))

model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu')) 
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax')) 

from keras.optimizers import Adam

init_lr = 1e-6
losses = []
lrs = []
for i in range(20):
  model = Sequential()
  model.add(Dense(512, input_shape=(784,)))
  model.add(Activation('relu')) 
  model.add(Dropout(0.2))
  model.add(Dense(512))
  model.add(Activation('relu'))
  model.add(Dropout(0.2))
  model.add(Dense(10))
  model.add(Activation('softmax')) 
  opt = Adam(lr=init_lr*2**i)

  model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=['acc'])


  hist = model.fit(X_train, Y_train, batch_size = 128, epochs=1)

  loss = hist.history['loss'][0]
  losses.append(loss)
  lrs.append(init_lr*2**i)

#lrs[0] = init_lr
fig, ax = plt.subplots(figsize = (10,7))
plt.plot(lrs,losses)
ax.set_xscale('log')

i = 1

model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu')) 
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax')) 
opt = Adam(lr=init_lr*2**i)

model.compile(loss='categorical_crossentropy', optimizer=opt,metrics=['acc'])


hist = model.fit(X_train, Y_train, batch_size = 128, epochs=1)

loss = hist.history['loss'][0]

hist.history['loss'][0]