n_hidden = hidden_nodes

nb_classes = 784

optimizer = 'RMSprop'

# optimizer = SGD(lr=0.001)

loss = 'mean_squared_error'

metrics = ['accuracy']

batch_size = 128

nb_epoch = number_epoch

if task_no in ['4','5']:

#laoding model

json_file = open('model.json', 'r')

loaded_model_json = json_file.read()

json_file.close()

loaded_model = model_from_json(loaded_model_json)

# load weights into new model

loaded_model.load_weights("model.h5")

print("Loaded model from disk")

loaded_model.compile(loss=loss, optimizer=optimizer, metrics=metrics)

else:

#initializing model

# input_img = Input(shape=(784,))

# hidden_output = Dense(n_hidden, activation='relu')(input_img)

# final_output = Dense(nb_classes, activation='linear')(hidden_output)

# model = Model(input=input_img, output=final_output)

# model.compile(loss=loss, optimizer=optimizer, metrics=metrics)

model = Sequential()

model.add(Dense(n_hidden, input_shape=(784,), activation='relu'))

model.add(Dense(nb_classes, activation='linear'))

model.compile(loss=loss, optimizer=optimizer, metrics=metrics)

#In all tasks as the target and input is the same we are passing input at both the places

if task_no == '1':

#task1

history = model.fit(train_input,train_input, batch_size=batch_size, nb_epoch=nb_epoch, verbose=2, validation_data=(test_input, test_input), shuffle=True)

return history

elif task_no == '2':

#task2

history = model.fit(train_input,train_input, batch_size=batch_size, nb_epoch=nb_epoch, verbose=2, validation_data=(test_input, test_input), shuffle=True)

train_score = model.evaluate(train_input, train_input, verbose=1)

test_score = model.evaluate(test_input, test_input, verbose=1)

return [train_score[0],test_score[0]]

elif task_no == '3':

#task3

history = model.fit(train_input,train_input, batch_size=batch_size, nb_epoch=nb_epoch, verbose=2, shuffle=True)

#saving mdoel to json file

model_json = model.to_json()

with open("model.json", "w") as json_file:

json_file.write(model_json)

# serialize weights to HDF5

model.save_weights("model.h5")

print("Saved model to disk")

weights, biases = model.layers[0].get_weights()

return weights

elif task_no in ['4','5']:

#task4

output = loaded_model.predict(test_input)

#dsiplaying loss

fig1=plt.figure('Loss')

ax1=fig1.add_subplot(111)

ax1.plot(epoch, train_score, 'b-', label='train')

ax1.plot(epoch, test_score, 'g-', label='validation')

ax1.set_xlabel(xlabel)

ax1.set_ylabel(ylabel)

plt.legend()

#dsiplaying weights

fig, axes = plt.subplots(10, 10, figsize=(12, 12))

fig.suptitle("Weights", fontsize=16)

for i in range(100):

row, column = divmod(i, 10)

axes[row, column].imshow(weights[:, i].reshape(28, 28), cmap=plt.cm.gray)

axes[row, column].axis('off') # get rid of tick marks/labels

#displaying input and output image

fig1, axes1 = plt.subplots(10, 10, figsize=(12, 12))

fig1.suptitle("Input", fontsize=16)

fig2, axes2 = plt.subplots(10, 10, figsize=(12, 12))

fig2.suptitle("Output", fontsize=16)

for i in range(100):

row, column = divmod(i, 10)

axes1[row, column].imshow(_input[i,:].reshape(28, 28), cmap=plt.cm.gray)

axes1[row, column].axis('off') # get rid of tick marks/labels

axes2[row, column].imshow(output[i,:].reshape(28, 28), cmap=plt.cm.gray)

axes2[row, column].axis('off') # get rid of tick marks/labels

#displaying input and output image

fig1, axes1 = plt.subplots(10, 10, figsize=(12, 12))

fig1.suptitle("First 100 Eigen vectors of inputs", fontsize=16)

fig2, axes2 = plt.subplots(10, 10, figsize=(12, 12))

fig2.suptitle("First 100 Eigen vectors of outputs", fontsize=16)

for i in range(100):

row, column = divmod(i, 10)

axes1[row, column].imshow(_input[:,i].reshape(28, 28), cmap=plt.cm.gray)

axes1[row, column].axis('off') # get rid of tick marks/labels

axes2[row, column].imshow(output[:,i].reshape(28, 28), cmap=plt.cm.gray)

axes2[row, column].axis('off') # get rid of tick marks/labels

# mean center the data

data -= data.mean(axis=0)

# calculate the covariance matrix

cov_mat = np.cov(data, rowvar=False)

# calculate eigenvectors & eigenvalues of the covariance matrix

# use 'eigh' rather than 'eig' since cov_mat is symmetric,

# the performance gain is substantial

eign_vals, eign_vecs = la.eigh(cov_mat)

# sort eigenvalue in decreasing order

idx = np.argsort(eign_vals)[::-1]

eign_vecs = eign_vecs[:,idx]

# sort eigenvectors according to same index

eign_vals = eign_vals[idx]

# select the first 100 eigenvectors

eign_vecs = eign_vecs[:, :100]

# carry out the transformation on the data using eigenvectors

# and return the re-scaled data, eigenvalues, and eigenvectors

print(eign_vecs.shape)

#reading and making input

i = 1

files = []

for filename in os.listdir(path):

if isfile(join(path,filename)):

files.append(filename)

files = np.array(files)

#shuffling files

np.random.shuffle(files)

files = files.tolist()

for filename in files:

img = scipy.misc.imread(join(path,filename)).astype(np.float32) #read image and convert to float

img = img.reshape(1,-1) #reshape to column vector

if i == 1:

P = np.array(img)

i = i + 1

else:

#adding each image to final input

P = np.concatenate((P,img),axis=0)

P = np.divide(P,255)

print(P.shape)