#!/usr/bin/env python
# coding: utf-8

# ## Standard (Fully Connected) Neural Network

# In[1]:


#Use in Markup cell type
#![alt text](imagename.png "Title")  


# ### Implementing Fully connected Neural Net

# #### Loading Required packages and Data

# In[2]:


###1. Load Data and Splot Data
from keras.datasets import mnist
from keras.models import Sequential 
from keras.layers.core import Dense, Activation
from keras.utils import np_utils
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()


# #### Preprocessing

# In[3]:


import matplotlib.pyplot as plt
n = 10  # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
    # display original
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(X_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()
plt.close()


# In[4]:


print("Previous X_train shape: {} \nPrevious Y_train shape:{}".format(X_train.shape, Y_train.shape))
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
classes = 10
Y_train = np_utils.to_categorical(Y_train, classes)     
Y_test = np_utils.to_categorical(Y_test, classes)
print("New X_train shape: {} \nNew Y_train shape:{}".format(X_train.shape, Y_train.shape))


# #### Setting up parameters

# In[5]:


input_size = 784
batch_size = 200   
hidden1 = 400
hidden2 = 20
epochs = 2


# #### Building the FCN Model

# In[6]:


###4.Build the model
model = Sequential()     
model.add(Dense(hidden1, input_dim=input_size, activation='relu'))
# output = relu (dot (W, input) + bias)
model.add(Dense(hidden2, activation='relu'))
model.add(Dense(classes, activation='softmax')) 

# Compilation
model.compile(loss='categorical_crossentropy', 
    metrics=['accuracy'], optimizer='sgd')
model.summary()


# #### Training The Model

# In[7]:


# Fitting on Data
model.fit(X_train, Y_train, batch_size=batch_size, epochs=10, verbose=2)
###5.Test 


# #### Testing The Model

# In[8]:


score = model.evaluate(X_test, Y_test, verbose=1)
print('\n''Test accuracy:', score[1])
mask = range(10,20)
X_valid = X_test[mask]
y_pred = model.predict_classes(X_valid)
print(y_pred)
plt.figure(figsize=(20, 4))
for i in range(n):
    # display original
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(X_valid[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()
plt.close()


# In[ ]: