# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Reset environment for a new run
% reset -f

# Load Libraries
from os.path import join # for joining file pathnames
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

# Set Pandas display options
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format

features = np.array(range(10))
features = features[:, np.newaxis]
# Create labels by adding noise distributed around 0
labels = features + np.random.random(size=[10,1]) - 0.5

# Visualize the data
plt.scatter(features,labels)

# Delete any existing assignment to "model"
model = None

# Use a sequential model
model = keras.Sequential()

# Add a layer with 1 neuron. Use the popular "tanh" activation function
model.add(keras.layers.Dense(units=1,             # 1 neuron
                             activation='tanh',   # 'tanh'
                             input_dim=1))         # number of feature cols=1

# Model calculates loss using mean-square error (MSE)
# Model trains using Adam optimizer with learning rate = 0.001
model.compile(optimizer=tf.optimizers.Adam(0.001),
              loss='mse',
             )

print(model.summary())

model.fit(x=features,
          y=labels,
          epochs=10,    # train for 10 epochs
          batch_size=10,# use 10 examples per batch
          verbose=1)    # verbose=1 prints progress per epoch

model = None
model = keras.Sequential()
model.add(keras.layers.Dense(1, activation='linear', input_dim=1))
model.compile(optimizer=tf.optimizers.Adam(0.001), loss='mse')
trainHistory = model.fit(features, labels, epochs=10, batch_size=1, verbose=1)
# Plot loss curve
plt.plot(trainHistory.history['loss'])
plt.title('Loss Curves')

model = None
model = keras.Sequential()
model.add(keras.layers.Dense(1, activation='linear', input_dim=1))
model.compile(optimizer=tf.optimizers.Adam(0.1), loss='mse')
model.fit(features, labels, epochs=5, batch_size=1, verbose=1)

# get predictions
featuresPred = model.predict(features, verbose=1)
# Plot original features and predicted values
featuresPred = np.transpose(featuresPred)
plt.scatter(range(10), labels, c="blue")
plt.scatter(range(10), featuresPred, c="red")
plt.legend(["Original", "Predicted"])

# create data with large values
features = np.array(range(50))
# generate labels
labels = features + np.random.random(features.shape) - 0.5

# Transpose data for input
[features, labels] = [features.transpose(), labels.transpose()]

plt.scatter(range(len(features)), features)

# Train on raw data
model = None
model = keras.Sequential()
model.add(keras.layers.Dense(1, input_dim=1, activation='linear'))
model.compile(optimizer=keras.optimizers.SGD(0.01), loss='mse')
model.fit(features, labels, epochs=5, batch_size=10, verbose=1)