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model_generation.py
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import numpy as np
import os
from tensorflow.keras.models import Sequential, model_from_json
from tensorflow.keras.layers import Dense, Dropout, Flatten, BatchNormalization, Activation
from keras.utils.np_utils import to_categorical as tcg
from keras.layers.convolutional import MaxPooling2D, Convolution2D
import matplotlib.pyplot as plt
from get_data import *
#Accuracy -> 68.61% Train. 65.92% Test
#Saved as CNN_model.json with weights as CNN_weights.h5
#Model Structure defined in CNN_Model_Details
#Model Loss graph is CNN_Loss_graph
def split_data(train_data, test_data):
xtr = []
xte = []
ytr = []
yte = []
for i, j in train_data:
xtr.append(i)
ytr.append(j)
for i, j in test_data:
xte.append(i)
yte.append(j)
xtr = np.array(xtr)
xte = np.array(xte)
ytr = np.array(ytr)
yte = np.array(yte)
xtr = xtr.reshape(xtr.shape[0], xtr.shape[1], xtr.shape[2], 1).astype('float32')/32
xte = xte.reshape(xte.shape[0], xte.shape[1], xte.shape[2], 1).astype('float32')/32
ytr = tcg(ytr)
yte = tcg(yte)
return xtr, ytr, xte, yte
def model_structure(xtr, ytr, xte, yte):
model = Sequential()
model.add(Convolution2D(filters=16, kernel_size=(3, 3), padding="SAME"))
model.add(Convolution2D(filters=16, kernel_size=(3, 3), padding="SAME"))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.35))
model.add(Convolution2D(filters=32, kernel_size=(3, 3), padding="SAME"))
model.add(Convolution2D(filters=32, kernel_size=(3, 3), padding="SAME"))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.35))
model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding="SAME"))
model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding="SAME"))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.35))
model.add(Convolution2D(filters=128, kernel_size=(3, 3)))
model.add(Convolution2D(filters=128, kernel_size=(3, 3)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(7, activation="softmax"))
model.compile(optimizer="Adam", loss="categorical_crossentropy", metrics=['accuracy'])
history = model.fit(x=xtr, y=ytr, validation_data=(xte, yte), epochs=200, batch_size=256)
print("<-------------------Model Trained. Saving as JSON File------------------->")
model_json = model.to_json()
with open(resource_path("Model/CNN_Model.json"), "w") as json_file:
json_file.write(model_json)
model.save_weights(resource_path("Model/CNN_Weights.h5"))
plot_graph(history)
def plot_graph(history):
plt.plot(history.history["loss"])
plt.plot(history.history["val_loss"])
plt.legend(["Train", "Test"], loc="upper left")
plt.title("Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.savefig(resource_path('Model/CNN_Loss.png'))
plt.close()
def load_model():
json_file = open(resource_path("Model/CNN_Model.json"), 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights(resource_path("Model/CNN_Weights.h5"))
return loaded_model
def create_cnn_model():
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
train_data, test_data = get_file_path()
xtr, ytr, xte, yte = split_data(train_data, test_data)
print("<-------------------Training Model------------------->")
model_structure(xtr, ytr, xte, yte)