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

# # Gridfonts
# 
# This notebook explores the datasets "gridfonts" and "figure-ground-a" based on Douglas Hofstadter and colleagues [Letter Spririt](https://cogsci.indiana.edu/letterspirit.html) project. See also [gridfonts](https://cogsci.indiana.edu/gridfonts.html).
# 
# This data was used in both Gary McGraw's and Douglas Blank's theses to train neural networks. See section 6.3.2 of [McGraw's thesis](http://goosie.cogsci.indiana.edu/farg/mcgrawg/thesis.html), and Blank's thesis [Learning to See Analogies: a Connectionist Exploration](https://repository.brynmawr.edu/compsci_pubs/78/).
# 
# * `figure-ground-a` is composed of letter a's 
# * `gridfont` is composed of entire alphabets

# In[1]:


import conx as cx


# ## Letterpart Analogies

# First, we download and load the figure_ground_a dataset:

# In[2]:


ds = cx.Dataset.get("figure_ground_a")


# In[3]:


ds.summary()


# The figure_ground_a database is composed of 229 a's where:
# 
# * input - a 153 integer "picture" of an "a" representing 17 rows and 9 columns
# * targets - two 153 pictures, representing the "brim" of the letter a, and the "body"
# 
# Let's display the letters in a graphical manner:

# In[4]:


def display_letter_parts(letter, brim, body):
    print("Letter        Brim          Body")
    for row in range(17):
        for col in range(9):
            print("." if letter[row * 9 + col] == 0 else "X", end="")
        print("     ", end="")
        for col in range(9):
            print("." if brim[row * 9 + col] == 0 else "X", end="")
        print("     ", end="")
        for col in range(9):
            print("." if body[row * 9 + col] == 0 else "X", end="")
        print()


# In[5]:


display_letter_parts(ds.inputs[0], ds.targets[0][0], ds.targets[0][1])


# Let's design some networks that attempt to replicate the brim and body given the original gridfont letter.
# 
# First, let's change the format of the dataset input so that we can treat the input as a 2D 17 x 9 image rather than merely a vector of 153 values. We actually make it a 3D shape, the third dimension representing color (which it doesn't have).

# In[6]:


ds.inputs.reshape(0, (17, 9, 1))


# In[7]:


cx.shape(ds.inputs[0])


# In[8]:


cx.array_to_image(ds.inputs[0], scale=10)


# In[9]:


cx.array_to_image(ds.targets[0][0], scale=10, shape=(17,9))


# In[10]:


cx.array_to_image(ds.targets[0][1], scale=10, shape=(17,9))


# We can now use a Convolutional Layer to better process the letter "picture". Notice that we flatten the output of the Convolutional layer to bring the output back into a single dimension.

# In[11]:


net = cx.Network("Letterpart Analogies")
net.add(cx.Layer("input", (17, 9, 1)))
net.add(cx.Conv2DLayer("conv", 2, (2,2)))
net.add(cx.FlattenLayer("flatten"))
net.add(cx.Layer("hidden", 50, activation="relu"))
net.add(cx.Layer("brim", 153, vshape=(17, 9), activation="sigmoid"))
net.add(cx.Layer("body", 153, vshape=(17, 9), activation="sigmoid"))

net.connect("input", "conv")
net.connect("conv", "flatten")
net.connect("flatten", "hidden")
net.connect("hidden", "brim")
net.connect("hidden", "body")
net.compile(error="mse", optimizer="adam")


# We associate the dataset to the network, and save 10 pictures for testing.

# In[12]:


net.set_dataset(ds)
net.dataset.split(10)


# In[13]:


net.dashboard()


# Now, we are ready to train the network to replicate the letter parts.

# In[14]:


net.train(1000, accuracy=1.0, tolerance=0.4, batch_size=128, report_rate=100)


# In[15]:


net.plot(".*acc")


# In[16]:


net.picture()


# In[17]:


results = net.train_one(ds.inputs[8], ds.targets[1])


# ## Gridfonts - leter recognition
# 

# In[18]:


ds = cx.Dataset.get("gridfonts")


# In[19]:


ds.summary()


# In[20]:


net = cx.Network("Gridfonts")
net.add(cx.Layer("inputs", (25,9)))
net.add(cx.FlattenLayer("flatten"))
net.add(cx.Layer("hiddens", 20, activation="sigmoid"))
net.add(cx.Layer("outputs", (25, 9), activation="relu"))
net.connect()
net.compile(error="mse", optimizer="adam")


# In[21]:


net.set_dataset(ds)


# In[22]:


display(net)
output = net.propagate(ds.inputs[26])
print(ds.labels[26])


# In[23]:


def display_letter(letter):
    print("Letter")
    for row in range(25):
        for col in range(9):
            print("." if letter[row][col] == 0 else "X", end="")
        print()


# In[24]:


display_letter(ds.inputs[0])


# In[25]:


cx.array_to_image(ds.inputs[0], scale=10)


# In[26]:


cx.array_to_image(ds.targets[0], scale=10)


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