def Text_Classification(x_train, y_train, x_test, y_test, batch_size=128,
EMBEDDING_DIM=50,MAX_SEQUENCE_LENGTH = 500, MAX_NB_WORDS = 75000,
GloVe_dir="", GloVe_file = "glove.6B.50d.txt",
sparse_categorical=True, random_deep=[3, 3, 3], epochs=[500, 500, 500], plot=False,
min_hidden_layer_dnn=1, max_hidden_layer_dnn=8, min_nodes_dnn=128, max_nodes_dnn=1024,
min_hidden_layer_rnn=1, max_hidden_layer_rnn=5, min_nodes_rnn=32, max_nodes_rnn=128,
min_hidden_layer_cnn=3, max_hidden_layer_cnn=10, min_nodes_cnn=128, max_nodes_cnn=512,
random_state=42, random_optimizor=True, dropout=0.5,no_of_classes=0):
"""
Text_Classification(x_train, y_train, x_test, y_test, batch_size=128,
EMBEDDING_DIM=50,MAX_SEQUENCE_LENGTH = 500, MAX_NB_WORDS = 75000,
GloVe_dir="", GloVe_file = "glove.6B.50d.txt",
sparse_categorical=True, random_deep=[3, 3, 3], epochs=[500, 500, 500], plot=False,
min_hidden_layer_dnn=1, max_hidden_layer_dnn=8, min_nodes_dnn=128, max_nodes_dnn=1024,
min_hidden_layer_rnn=1, max_hidden_layer_rnn=5, min_nodes_rnn=32, max_nodes_rnn=128,
min_hidden_layer_cnn=3, max_hidden_layer_cnn=10, min_nodes_cnn=128, max_nodes_cnn=512,
random_state=42, random_optimizor=True, dropout=0.5):
Parameters
----------
batch_size : Integer, , optional
Number of samples per gradient update. If unspecified, it will default to 128
MAX_NB_WORDS: int, optional
Maximum number of unique words in datasets, it will default to 75000.
GloVe_dir: String, optional
Address of GloVe or any pre-trained directory, it will default to null which glove.6B.zip will be download.
GloVe_dir: String, optional
Which version of GloVe or pre-trained word emending will be used, it will default to glove.6B.50d.txt.
NOTE: if you use other version of GloVe EMBEDDING_DIM must be same dimensions.
sparse_categorical: bool.
When target's dataset is (n,1) should be True, it will default to True.
random_deep: array of int [3], optional
Number of ensembled model used in RMDL random_deep[0] is number of DNN, random_deep[1] is number of RNN, random_deep[0] is number of CNN, it will default to [3, 3, 3].
epochs: array of int [3], optional
Number of epochs in each ensembled model used in RMDL epochs[0] is number of epochs used in DNN, epochs[1] is number of epochs used in RNN, epochs[0] is number of epochs used in CNN, it will default to [500, 500, 500].
plot: bool, optional
True: shows confusion matrix and accuracy and loss
min_hidden_layer_dnn: Integer, optional
Lower Bounds of hidden layers of DNN used in RMDL, it will default to 1.
max_hidden_layer_dnn: Integer, optional
Upper bounds of hidden layers of DNN used in RMDL, it will default to 8.
min_nodes_dnn: Integer, optional
Lower bounds of nodes in each layer of DNN used in RMDL, it will default to 128.
max_nodes_dnn: Integer, optional
Upper bounds of nodes in each layer of DNN used in RMDL, it will default to 1024.
min_hidden_layer_rnn: Integer, optional
Lower Bounds of hidden layers of RNN used in RMDL, it will default to 1.
min_hidden_layer_rnn: Integer, optional
Upper Bounds of hidden layers of RNN used in RMDL, it will default to 5.
min_nodes_rnn: Integer, optional
Lower bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL, it will default to 32.
max_nodes_rnn: Integer, optional
Upper bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL, it will default to 128.
min_hidden_layer_cnn: Integer, optional
Lower Bounds of hidden layers of CNN used in RMDL, it will default to 3.
max_hidden_layer_cnn: Integer, optional
Upper Bounds of hidden layers of CNN used in RMDL, it will default to 10.
min_nodes_cnn: Integer, optional
Lower bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL, it will default to 128.
min_nodes_cnn: Integer, optional
Upper bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL, it will default to 512.
random_state : Integer, optional
RandomState instance or None, optional (default=None)
If Integer, random_state is the seed used by the random number generator;
random_optimizor : bool, optional
If False, all models use adam optimizer. If True, all models use random optimizers. it will default to True
dropout: Float, optional
between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
"""
np.random.seed(random_state)
glove_directory = GloVe_dir
GloVe_file = GloVe_file
print("Done1")
GloVe_needed = random_deep[1] != 0 or random_deep[2] != 0
# example_input = [0,1,3]
# example_output :
#
# [[1 0 0 0]
# [0 1 0 0]
# [0 0 0 1]]
def one_hot_encoder(value, label_data_):
label_data_[value] = 1
return label_data_
def _one_hot_values(labels_data):
encoded = [0] * len(labels_data)
for index_no, value in enumerate(labels_data):
max_value = [0] * (np.max(labels_data) + 1)
encoded[index_no] = one_hot_encoder(value, max_value)
return np.array(encoded)
if not isinstance(y_train[0], list) and not isinstance(y_train[0], np.ndarray) and not sparse_categorical::
#checking if labels are one hot or not otherwise dense_layer will give shape error
print("converted_into_one_hot")
y_train = _one_hot_values(y_train)
y_test = _one_hot_values(y_test)
if GloVe_needed:
if glove_directory == "":
GloVe_directory = GloVe.download_and_extract()
GloVe_DIR = os.path.join(GloVe_directory, GloVe_file)
else:
GloVe_DIR = os.path.join(glove_directory, GloVe_file)
if not os.path.isfile(GloVe_DIR):
print("Could not find %s Set GloVe Directory in Global.py ", GloVe)
exit()
G.setup()
if random_deep[0] != 0:
x_train_tfidf, x_test_tfidf = txt.loadData(x_train, x_test,MAX_NB_WORDS=MAX_NB_WORDS)
if random_deep[1] != 0 or random_deep[2] != 0 :
print(GloVe_DIR)
x_train_embedded, x_test_embedded, word_index, embeddings_index = txt.loadData_Tokenizer(x_train, x_test,GloVe_DIR,MAX_NB_WORDS,MAX_SEQUENCE_LENGTH,EMBEDDING_DIM)
del x_train
del x_test
gc.collect()
y_pr = []
History = []
score = []
if no_of_classes==0:
#checking no_of_classes
#np.max(data)+1 will not work for one_hot encoding labels
if sparse_categorical:
number_of_classes = np.max(y_train) + 1
else:
number_of_classes = len(y_train[0])
else:
number_of_classes = no_of_classes
print(number_of_classes)
i = 0
while i < random_deep[0]:
# model_DNN.append(Sequential())
try:
print("DNN " + str(i))
filepath = "weights\weights_DNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
model_DNN, model_tmp = BuildModel.Build_Model_DNN_Text(x_train_tfidf.shape[1],
number_of_classes,
sparse_categorical,
min_hidden_layer_dnn,
max_hidden_layer_dnn,
min_nodes_dnn,
max_nodes_dnn,
random_optimizor,
dropout)
model_history = model_DNN.fit(x_train_tfidf, y_train,
validation_data=(x_test_tfidf, y_test),
epochs=epochs[0],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
History.append(model_history)
model_tmp.load_weights(filepath)
if sparse_categorical:
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr_ = model_tmp.predict_classes(x_test_tfidf,
batch_size=batch_size)
y_pr.append(np.array(y_pr_))
score.append(accuracy_score(y_test, y_pr_))
else:
model_tmp.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr_ = model_tmp.predict(x_test_tfidf,
batch_size=batch_size)
y_pr_ = np.argmax(y_pr_, axis=1)
y_pr.append(np.array(y_pr_))
y_test_temp = np.argmax(y_test, axis=1)
score.append(accuracy_score(y_test_temp, y_pr_))
# print(y_proba)
i += 1
del model_tmp
del model_DNN
except Exception as e:
print("Check the Error \n {} ".format(e))
print("Error in model", i, "try to re-generate another model")
if max_hidden_layer_dnn > 3:
max_hidden_layer_dnn -= 1
if max_nodes_dnn > 256:
max_nodes_dnn -= 8
try:
del x_train_tfidf
del x_test_tfidf
gc.collect()
except:
pass
i=0
while i < random_deep[1]:
try:
print("RNN " + str(i))
filepath = "weights\weights_RNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
model_RNN, model_tmp = BuildModel.Build_Model_RNN_Text(word_index,
embeddings_index,
number_of_classes,
MAX_SEQUENCE_LENGTH,
EMBEDDING_DIM,
sparse_categorical,
min_hidden_layer_rnn,
max_hidden_layer_rnn,
min_nodes_rnn,
max_nodes_rnn,
random_optimizor,
dropout)
model_history = model_RNN.fit(x_train_embedded, y_train,
validation_data=(x_test_embedded, y_test),
epochs=epochs[1],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
History.append(model_history)
if sparse_categorical:
model_tmp.load_weights(filepath)
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
y_pr_ = model_tmp.predict_classes(x_test_embedded, batch_size=batch_size)
y_pr.append(np.array(y_pr_))
score.append(accuracy_score(y_test, y_pr_))
else:
model_tmp.load_weights(filepath)
model_tmp.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
y_pr_ = model_tmp.predict(x_test_embedded, batch_size=batch_size)
y_pr_ = np.argmax(y_pr_, axis=1)
y_pr.append(np.array(y_pr_))
y_test_temp = np.argmax(y_test, axis=1)
score.append(accuracy_score(y_test_temp, y_pr_))
i += 1
del model_tmp
del model_RNN
gc.collect()
except:
print("Error in model", i, "try to re-generate another model")
if max_hidden_layer_rnn > 3:
max_hidden_layer_rnn -= 1
if max_nodes_rnn > 64:
max_nodes_rnn -= 2
gc.collect()
i = 0
while i < random_deep[2]:
try:
print("CNN " + str(i))
model_CNN, model_tmp = BuildModel.Build_Model_CNN_Text(word_index,
embeddings_index,
number_of_classes,
MAX_SEQUENCE_LENGTH,
EMBEDDING_DIM,
sparse_categorical,
min_hidden_layer_cnn,
max_hidden_layer_cnn,
min_nodes_cnn,
max_nodes_cnn,
random_optimizor,
dropout)
filepath = "weights\weights_CNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
model_history = model_CNN.fit(x_train_embedded, y_train,
validation_data=(x_test_embedded, y_test),
epochs=epochs[2],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
History.append(model_history)
model_tmp.load_weights(filepath)
if sparse_categorical:
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
else:
model_tmp.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
y_pr_ = model_tmp.predict(x_test_embedded, batch_size=batch_size)
y_pr_ = np.argmax(y_pr_, axis=1)
y_pr.append(np.array(y_pr_))
if sparse_categorical:
score.append(accuracy_score(y_test, y_pr_))
else:
y_test_temp = np.argmax(y_test, axis=1)
score.append(accuracy_score(y_test_temp, y_pr_))
i += 1
del model_tmp
del model_CNN
gc.collect()
except:
print("Error in model", i, "try to re-generate an other model")
if max_hidden_layer_cnn > 5:
max_hidden_layer_cnn -= 1
if max_nodes_cnn > 128:
max_nodes_cnn -= 2
min_nodes_cnn -= 1
gc.collect()
y_proba = np.array(y_pr).transpose()
final_y = []
for i in range(0, y_proba.shape[0]):
a = np.array(y_proba[i, :])
a = collections.Counter(a).most_common()[0][0]
final_y.append(a)
if sparse_categorical:
F_score = accuracy_score(y_test, final_y)
F1 = precision_recall_fscore_support(y_test, final_y, average='micro')
F2 = precision_recall_fscore_support(y_test, final_y, average='macro')
F3 = precision_recall_fscore_support(y_test, final_y, average='weighted')
cnf_matrix = confusion_matrix(y_test, final_y)
# Compute confusion matrix
# Plot non-normalized confusion matrix
if plot:
classes = list(range(0, np.max(y_test)+1))
Plot.plot_confusion_matrix(cnf_matrix, classes=classes,
title='Confusion matrix, without normalization')
# Plot normalized confusion matrix
Plot.plot_confusion_matrix(cnf_matrix, classes=classes, normalize=True,
title='Normalized confusion matrix')
else:
y_test_temp = np.argmax(y_test, axis=1)
F_score = accuracy_score(y_test_temp, final_y)
F1 = precision_recall_fscore_support(y_test_temp, final_y, average='micro')
F2 = precision_recall_fscore_support(y_test_temp, final_y, average='macro')
F3 = precision_recall_fscore_support(y_test_temp, final_y, average='weighted')
if plot:
Plot.RMDL_epoch(History)
print(y_proba.shape)
print("Accuracy of",len(score),"models:",score)
print("Accuracy:",F_score)
print("F1_Micro:",F1)
print("F1_Macro:",F2)
print("F1_weighted:",F3)
def Image_Classification(x_train, y_train, x_test, y_test, shape, batch_size=128,
sparse_categorical=True, random_deep=[3, 3, 3], epochs=[500, 500, 500], plot=False,
min_hidden_layer_dnn=1, max_hidden_layer_dnn=8, min_nodes_dnn=128, max_nodes_dnn=1024,
max_hidden_layer_rnn=5, min_nodes_rnn=32, max_nodes_rnn=128,
min_hidden_layer_cnn=3, max_hidden_layer_cnn=10, min_nodes_cnn=128, max_nodes_cnn=512,
random_state=42, random_optimizor=True, dropout=0.05):
"""
def Image_Classification(x_train, y_train, x_test, y_test, shape, batch_size=128,
sparse_categorical=True, random_deep=[3, 3, 3], epochs=[500, 500, 500], plot=False,
min_hidden_layer_dnn=1, max_hidden_layer_dnn=8, min_nodes_dnn=128, max_nodes_dnn=1024,
min_hidden_layer_rnn=1, max_hidden_layer_rnn=5, min_nodes_rnn=32, max_nodes_rnn=128,
min_hidden_layer_cnn=3, max_hidden_layer_cnn=10, min_nodes_cnn=128, max_nodes_cnn=512,
random_state=42, random_optimizor=True, dropout=0.05):
Parameters
----------
x_train : string
input X for training
y_train : int
input Y for training
x_test : string
input X for testing
x_test : int
input Y for testing
shape : np.shape
shape of image. The most common situation would be a 2D input with shape (batch_size, input_dim).
batch_size : Integer, , optional
Number of samples per gradient update. If unspecified, it will default to 128
MAX_NB_WORDS: int, optional
Maximum number of unique words in datasets, it will default to 75000.
GloVe_dir: String, optional
Address of GloVe or any pre-trained directory, it will default to null which glove.6B.zip will be download.
GloVe_dir: String, optional
Which version of GloVe or pre-trained word emending will be used, it will default to glove.6B.50d.txt.
NOTE: if you use other version of GloVe EMBEDDING_DIM must be same dimensions.
sparse_categorical: bool.
When target's dataset is (n,1) should be True, it will default to True.
random_deep: array of int [3], optional
Number of ensembled model used in RMDL random_deep[0] is number of DNN, random_deep[1] is number of RNN, random_deep[0] is number of CNN, it will default to [3, 3, 3].
epochs: array of int [3], optional
Number of epochs in each ensembled model used in RMDL epochs[0] is number of epochs used in DNN, epochs[1] is number of epochs used in RNN, epochs[0] is number of epochs used in CNN, it will default to [500, 500, 500].
plot: bool, optional
True: shows confusion matrix and accuracy and loss
min_hidden_layer_dnn: Integer, optional
Lower Bounds of hidden layers of DNN used in RMDL, it will default to 1.
max_hidden_layer_dnn: Integer, optional
Upper bounds of hidden layers of DNN used in RMDL, it will default to 8.
min_nodes_dnn: Integer, optional
Lower bounds of nodes in each layer of DNN used in RMDL, it will default to 128.
max_nodes_dnn: Integer, optional
Upper bounds of nodes in each layer of DNN used in RMDL, it will default to 1024.
min_hidden_layer_rnn: Integer, optional
Lower Bounds of hidden layers of RNN used in RMDL, it will default to 1.
min_hidden_layer_rnn: Integer, optional
Upper Bounds of hidden layers of RNN used in RMDL, it will default to 5.
min_nodes_rnn: Integer, optional
Lower bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL, it will default to 32.
max_nodes_rnn: Integer, optional
Upper bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL, it will default to 128.
min_hidden_layer_cnn: Integer, optional
Lower Bounds of hidden layers of CNN used in RMDL, it will default to 3.
max_hidden_layer_cnn: Integer, optional
Upper Bounds of hidden layers of CNN used in RMDL, it will default to 10.
min_nodes_cnn: Integer, optional
Lower bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL, it will default to 128.
min_nodes_cnn: Integer, optional
Upper bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL, it will default to 512.
random_state : Integer, optional
RandomState instance or None, optional (default=None)
If Integer, random_state is the seed used by the random number generator;
random_optimizor : bool, optional
If False, all models use adam optimizer. If True, all models use random optimizers. it will default to True
dropout: Float, optional
between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
"""
if len(x_train) != len(y_train):
raise ValueError('shape of x_train and y_train must be equal'
'The x_train has ' + str(len(x_train)) +
'The x_train has' +
str(len(y_train)))
if len(x_test) != len(y_test):
raise ValueError('shape of x_test and y_test must be equal '
'The x_train has ' + str(len(x_test)) +
'The y_test has ' +
str(len(y_test)))
np.random.seed(random_state)
G.setup()
y_proba = []
score = []
history_ = []
if sparse_categorical:
number_of_classes = np.max(y_train)+1
else:
number_of_classes = np.shape(y_train)[0]
i =0
while i < random_deep[0]:
try:
print("DNN ", i, "\n")
model_DNN, model_tmp = BuildModel.Build_Model_DNN_Image(shape,
number_of_classes,
sparse_categorical,
min_hidden_layer_dnn,
max_hidden_layer_dnn,
min_nodes_dnn,
max_nodes_dnn,
random_optimizor,
dropout)
filepath = "weights\weights_DNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_accuracy',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
history = model_DNN.fit(x_train, y_train,
validation_data=(x_test, y_test),
epochs=epochs[0],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
history_.append(history)
model_tmp.load_weights(filepath)
if sparse_categorical == 0:
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
else:
model_tmp.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr = model_tmp.predict_classes(x_test, batch_size=batch_size)
y_proba.append(np.array(y_pr))
score.append(accuracy_score(y_test, y_pr))
i = i + 1
del model_tmp
del model_DNN
gc.collect()
except:
print("Error in model", i, "try to re-generate an other model")
if max_hidden_layer_dnn > 3:
max_hidden_layer_dnn -= 1
if max_nodes_dnn > 256:
max_nodes_dnn -= 8
i =0
while i < random_deep[1]:
try:
print("RNN ", i, "\n")
model_RNN, model_tmp = BuildModel.Build_Model_RNN_Image(shape,
number_of_classes,
sparse_categorical,
min_nodes_rnn,
max_nodes_rnn,
random_optimizor,
dropout)
filepath = "weights\weights_RNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_accuracy',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
history = model_RNN.fit(x_train, y_train,
validation_data=(x_test, y_test),
epochs=epochs[1],
batch_size=batch_size,
verbose=2,
callbacks=callbacks_list)
model_tmp.load_weights(filepath)
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
history_.append(history)
y_pr = model_tmp.predict(x_test, batch_size=batch_size)
y_pr = np.argmax(y_pr, axis=1)
y_proba.append(np.array(y_pr))
score.append(accuracy_score(y_test, y_pr))
i = i+1
del model_tmp
del model_RNN
gc.collect()
except:
print("Error in model", i, " try to re-generate another model")
if max_hidden_layer_rnn > 3:
max_hidden_layer_rnn -= 1
if max_nodes_rnn > 64:
max_nodes_rnn -= 2
# reshape to be [samples][pixels][width][height]
i=0
while i < random_deep[2]:
try:
print("CNN ", i, "\n")
model_CNN, model_tmp = BuildModel.Build_Model_CNN_Image(shape,
number_of_classes,
sparse_categorical,
min_hidden_layer_cnn,
max_hidden_layer_cnn,
min_nodes_cnn,
max_nodes_cnn,
random_optimizor,
dropout)
filepath = "weights\weights_CNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
history = model_CNN.fit(x_train, y_train,
validation_data=(x_test, y_test),
epochs=epochs[2],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
history_.append(history)
model_tmp.load_weights(filepath)
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr = model_tmp.predict_classes(x_test, batch_size=batch_size)
y_proba.append(np.array(y_pr))
score.append(accuracy_score(y_test, y_pr))
i = i+1
del model_tmp
del model_CNN
gc.collect()
except:
print("Error in model", i, " try to re-generate another model")
if max_hidden_layer_cnn > 5:
max_hidden_layer_cnn -= 1
if max_nodes_cnn > 128:
max_nodes_cnn -= 2
min_nodes_cnn -= 1
y_proba = np.array(y_proba).transpose()
print(y_proba.shape)
final_y = []
for i in range(0, y_proba.shape[0]):
a = np.array(y_proba[i, :])
a = collections.Counter(a).most_common()[0][0]
final_y.append(a)
F_score = accuracy_score(y_test, final_y)
F1 = f1_score(y_test, final_y, average='micro')
F2 = f1_score(y_test, final_y, average='macro')
F3 = f1_score(y_test, final_y, average='weighted')
cnf_matrix = confusion_matrix(y_test, final_y)
# Compute confusion matrix
np.set_printoptions(precision=2)
if plot:
# Plot non-normalized confusion matrix
classes = list(range(0,np.max(y_test)+1))
Plot.plot_confusion_matrix(cnf_matrix, classes=classes,
title='Confusion matrix, without normalization')
Plot.plot_confusion_matrix(cnf_matrix, classes=classes,normalize=True,
title='Confusion matrix, without normalization')
if plot:
Plot.RMDL_epoch(history_)
print(y_proba.shape)
print("Accuracy of",len(score),"models:",score)
print("Accuracy:",F_score)
print("F1_Micro:",F1)
print("F1_Macro:",F2)
print("F1_weighted:",F3)
def train(x_train,
y_train,
x_val,
y_val,
class_weight=None,
batch_size=128,
embedding_dim=50,
max_seq_len=500,
max_num_words=75000,
glove_dir="",
glove_file="glove.6B.50d.txt",
sparse_categorical=True,
random_deep=[3, 3, 3],
epochs=[500, 500, 500],
plot=False,
min_hidden_layer_dnn=1,
max_hidden_layer_dnn=6,
min_nodes_dnn=128,
max_nodes_dnn=1024,
min_hidden_layer_rnn=1,
max_hidden_layer_rnn=5,
min_nodes_rnn=128,
max_nodes_rnn=512,
min_hidden_layer_cnn=3,
max_hidden_layer_cnn=10,
min_nodes_cnn=128,
max_nodes_cnn=512,
random_state=42,
random_optimizor=True,
dropout=0.5,
dnn_l2=0,
rnn_l2=0.01,
cnn_l2=0.01,
use_cuda=True,
use_bidirectional=True,
lr=1e-3):
"""
train(x_train, y_train, x_val, y_val, class_weight=None batch_size=128,
embedding_dim=50, max_seq_len=500, max_num_words=75000,
glove_dir="", glove_file="glove.6B.50d.txt",
sparse_categorical=True, random_deep=[3, 3, 3], epochs=[500, 500, 500], plot=False,
min_hidden_layer_dnn=1, max_hidden_layer_dnn=6, min_nodes_dnn=128, max_nodes_dnn=1024,
min_hidden_layer_rnn=1, max_hidden_layer_rnn=5, min_nodes_rnn=32, max_nodes_rnn=128,
min_hidden_layer_cnn=3, max_hidden_layer_cnn=10, min_nodes_cnn=128, max_nodes_cnn=512,
random_state=42, random_optimizor=True, dropout=0.5)
Parameters
----------
class_weight: dict, optional
Dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only).
This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
batch_size: int, optional
Number of samples per gradient update. It will default to 128.
embedding_dim: int, optional
Dimensionality of the vector representation (word embedding) of each token in the corpus.
It will default to 50.
max_seq_len: int, optional
Maximum number of words in a text to consider. It will default to 500.
max_num_words: int, optional
Maximum number of unique words in datasets. It will default to 75000.
glove_dir: string, optional
Path to GloVe or any pre-trained word embedding directory. It will default to the current
directory where glove.6B.zip should be downloaded.
glove_file: string, optional
Which version of GloVe or any pre-trained word embedding will be used. It will default to glove.6B.50d.txt.
NOTE: If you use other version of GloVe embedding_dim must be the same dimensions.
sparse_categorical: bool
When target's dataset is (n,1) should be True. It will default to True.
random_deep: array of int [3], optional
Number of ensembled models used in RMDL random_deep[0] is number of DNNs,
random_deep[1] is number of RNNs, random_deep[2] is number of CNNs. It will default to [3, 3, 3].
epochs: array of int [3], optional
Number of epochs in each ensembled model used in RMDL epochs[0] is number of epochs used in DNNs,
epochs[1] is number of epochs used in RNNs, epochs[0] is number of epochs used in CNNs. It will default to [500, 500, 500].
plot: bool, optional
Plot accuracies and losses of training and validation.
min_hidden_layer_dnn: int, optional
Lower Bounds of hidden layers of DNN used in RMDL. It will default to 1.
max_hidden_layer_dnn: int, optional
Upper bounds of hidden layers of DNN used in RMDL. It will default to 8.
min_nodes_dnn: int, optional
Lower bounds of nodes in each layer of DNN used in RMDL. It will default to 128.
max_nodes_dnn: int, optional
Upper bounds of nodes in each layer of DNN used in RMDL. It will default to 1024.
min_hidden_layer_rnn: int, optional
Lower Bounds of hidden layers of RNN used in RMDL. It will default to 1.
min_hidden_layer_rnn: int, optional
Upper Bounds of hidden layers of RNN used in RMDL. It will default to 5.
min_nodes_rnn: int, optional
Lower bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL. It will default to 32.
max_nodes_rnn: int, optional
Upper bounds of nodes (LSTM or GRU) in each layer of RNN used in RMDL. It will default to 128.
min_hidden_layer_cnn: int, optional
Lower Bounds of hidden layers of CNN used in RMDL. It will default to 3.
max_hidden_layer_cnn: int, optional
Upper Bounds of hidden layers of CNN used in RMDL. It will default to 10.
min_nodes_cnn: int, optional
Lower bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL. It will default to 128.
min_nodes_cnn: int, optional
Upper bounds of nodes (2D convolution layer) in each layer of CNN used in RMDL. It will default to 512.
random_state: int, optional
RandomState instance or None, optional (default=None)
If Integer, random_state is the seed used by the random number generator;
random_optimizor: bool, optional
If False, all models use adam optimizer. If True, all models use random optimizers. It will default to True
dropout: float, optional
between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
Returns
-------
history: list
List of training history dictionaries for models used.
"""
np.random.seed(random_state)
models_dir = "models"
util.setup()
history = []
if isinstance(y_train, list):
number_of_classes = len(set(y_train))
elif isinstance(y_train, np.ndarray):
number_of_classes = np.unique(y_train).shape[0]
if not isinstance(y_train[0], list) and not isinstance(y_train[0], np.ndarray) \
and not sparse_categorical and number_of_classes != 2:
# checking if labels are one hot or not otherwise dense_layer will give shape error
print("convert labels into one hot encoded representation")
y_train = txt.get_one_hot_values(y_train)
y_val = txt.get_one_hot_values(y_val)
glove_needed = random_deep[1] != 0 or random_deep[2] != 0
if glove_needed:
if glove_dir == "" and glove_file == "":
glove_dir = GloVe.download_and_extract()
glove_filepath = os.path.join(glove_dir, glove_file)
else:
glove_filepath = os.path.join(glove_dir, glove_file)
if not os.path.isfile(glove_filepath):
print(f"Could not find {GloVe} Set GloVe Directory in Global.py")
exit()
all_text = np.concatenate((x_train, x_val))
if random_deep[0] != 0:
all_text_tf_idf = txt.get_tf_idf_vectors(all_text,
max_num_words=max_num_words)
x_train_tf_idf = all_text_tf_idf[:len(x_train), ]
x_val_tf_idf = all_text_tf_idf[len(x_train):, ]
if random_deep[1] != 0 or random_deep[2] != 0:
print(glove_filepath)
all_text_tokenized, word_index = txt.tokenize(
all_text, max_num_words=max_num_words, max_seq_len=max_seq_len)
x_train_tokenized = all_text_tokenized[:len(x_train), ]
x_val_tokenized = all_text_tokenized[len(x_train):, ]
embeddings_index = txt.get_word_embedding_index(
glove_filepath, word_index)
del x_train
del x_val
gc.collect()
i = 0
while i < random_deep[0]:
try:
print(f"\nBuilding and Training DNN-{i}")
model_DNN, model_tmp_DNN = BuildModel.Build_Model_DNN_Text(
x_train_tf_idf.shape[1],
number_of_classes,
sparse_categorical,
min_hidden_layer_dnn,
max_hidden_layer_dnn,
min_nodes_dnn,
max_nodes_dnn,
random_optimizor,
dropout,
_l2=dnn_l2,
lr=lr)
model_arch_file = f"DNN_{i}.json"
model_weights_file = f"DNN_{i}.hdf5"
model_json = model_tmp_DNN.to_json()
with open(os.path.join(models_dir, model_arch_file),
"w") as model_json_file:
model_json_file.write(model_json)
checkpoint = ModelCheckpoint(os.path.join(models_dir,
model_weights_file),
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='min')
model_history = model_DNN.fit(x_train_tf_idf,
y_train,
validation_data=(x_val_tf_idf,
y_val),
epochs=epochs[0],
batch_size=batch_size,
callbacks=[checkpoint],
verbose=2,
class_weight=class_weight)
history.append(model_history)
i += 1
del model_DNN
gc.collect()
except Exception as e:
print(f"\nCheck the Error \n {e}")
print(
f"Error in DNN-{i} model trying to re-generate another model")
if max_hidden_layer_dnn > 3:
max_hidden_layer_dnn -= 1
if max_nodes_dnn > 256:
max_nodes_dnn -= 8
del x_train_tf_idf
del x_val_tf_idf
gc.collect()
i = 0
while i < random_deep[1]:
try:
print(f"\nBuilding and Training RNN-{i}")
model_RNN, model_tmp_RNN = BuildModel.Build_Model_RNN_Text(
word_index,
embeddings_index,
number_of_classes,
max_seq_len,
embedding_dim,
sparse_categorical,
min_hidden_layer_rnn,
max_hidden_layer_rnn,
min_nodes_rnn,
max_nodes_rnn,
random_optimizor,
dropout,
_l2=rnn_l2,
use_cuda=use_cuda,
use_bidirectional=use_bidirectional,
lr=lr)
model_arch_file = f"RNN_{i}.json"
model_weights_file = f"RNN_{i}.hdf5"
model_json = model_tmp_RNN.to_json()
with open(os.path.join(models_dir, model_arch_file),
"w") as model_json_file:
model_json_file.write(model_json)
checkpoint = ModelCheckpoint(os.path.join(models_dir,
model_weights_file),
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='min')
model_history = model_RNN.fit(x_train_tokenized,
y_train,
validation_data=(x_val_tokenized,
y_val),
epochs=epochs[1],
batch_size=batch_size,
callbacks=[checkpoint],
verbose=2,
class_weight=class_weight)
history.append(model_history)
i += 1
del model_RNN
gc.collect()
except Exception as e:
print(f"\nCheck the Error \n {e}")
print(
f"Error in RNN-{i} model trying to re-generate another model")
if max_hidden_layer_rnn > 3:
max_hidden_layer_rnn -= 1
if max_nodes_rnn > 64:
max_nodes_rnn -= 2
gc.collect()
i = 0
while i < random_deep[2]:
try:
print(f"\nBuilding and Training CNN-{i}")
model_CNN, model_tmp_CNN = BuildModel.Build_Model_CNN_Text(
word_index,
embeddings_index,
number_of_classes,
max_seq_len,
embedding_dim,
sparse_categorical,
min_hidden_layer_cnn,
max_hidden_layer_cnn,
min_nodes_cnn,
max_nodes_cnn,
random_optimizor,
dropout,
_l2=cnn_l2,
lr=lr)
model_arch_file = f"CNN_{i}.json"
model_weights_file = f"CNN_{i}.hdf5"
model_json = model_tmp_CNN.to_json()
with open(os.path.join(models_dir, model_arch_file),
"w") as model_json_file:
model_json_file.write(model_json)
checkpoint = ModelCheckpoint(os.path.join(models_dir,
model_weights_file),
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='min')
model_history = model_CNN.fit(x_train_tokenized,
y_train,
validation_data=(x_val_tokenized,
y_val),
epochs=epochs[2],
batch_size=batch_size,
callbacks=[checkpoint],
verbose=2,
class_weight=class_weight)
history.append(model_history)
i += 1
del model_CNN
gc.collect()
except Exception as e:
print(f"\nCheck the Error \n {e}")
print(
f"Error in CNN-{i} model trying to re-generate another model")
if max_hidden_layer_cnn > 5:
max_hidden_layer_cnn -= 1
if max_nodes_cnn > 128:
max_nodes_cnn -= 2
min_nodes_cnn -= 1
if plot:
plt.plot_history(history)
return history
def Text_Classification(x_train,
y_train,
x_test,
y_test,
batch_size=128,
EMBEDDING_DIM=50,
MAX_SEQUENCE_LENGTH=500,
MAX_NB_WORDS=75000,
GloVe_dir="",
GloVe_file="glove.6B.50d.txt",
sparse_categorical=True,
random_deep=[3, 3, 3],
epochs=[500, 500, 500],
plot=False,
min_hidden_layer_dnn=1,
max_hidden_layer_dnn=8,
min_nodes_dnn=128,
max_nodes_dnn=1024,
min_hidden_layer_rnn=1,
max_hidden_layer_rnn=5,
min_nodes_rnn=32,
max_nodes_rnn=128,
min_hidden_layer_cnn=3,
max_hidden_layer_cnn=10,
min_nodes_cnn=128,
max_nodes_cnn=512,
random_state=42,
random_optimizor=True,
dropout=0.5,
no_of_classes=0):
np.random.seed(random_state)
glove_directory = GloVe_dir
GloVe_file = GloVe_file
GloVe_needed = random_deep[1] != 0 or random_deep[2] != 0
# example_input = [0,1,3]
# example_output :
#
# [[1 0 0 0]
# [0 1 0 0]
# [0 0 0 1]]
def one_hot_encoder(value, datal):
datal[value] = 1
return datal
def _one_hot_values(labels_data):
encoded = [0] * len(labels_data)
for j, i in enumerate(labels_data):
max_value = [0] * (np.max(labels_data) + 1)
encoded[j] = one_hot_encoder(i, max_value)
return np.array(encoded)
if not isinstance(y_train[0], list) and not isinstance(
y_train[0], np.ndarray):
#checking if labels are one hot or not otherwise dense_layer will give shape error
print("converted_into_one_hot")
y_train = _one_hot_values(y_train)
y_test = _one_hot_values(y_test)
if GloVe_needed:
if glove_directory == "":
GloVe_directory = GloVe.download_and_extract()
GloVe_DIR = os.path.join(GloVe_directory, GloVe_file)
else:
GloVe_DIR = os.path.join(glove_directory, GloVe_file)
if not os.path.isfile(GloVe_DIR):
print("Could not find %s Set GloVe Directory in Global.py ", GloVe)
exit()
G.setup()
if random_deep[0] != 0:
x_train_tfidf, x_test_tfidf = txt.loadData(x_train,
x_test,
MAX_NB_WORDS=MAX_NB_WORDS)
if random_deep[1] != 0 or random_deep[2] != 0:
print(GloVe_DIR)
x_train_embedded, x_test_embedded, word_index, embeddings_index = txt.loadData_Tokenizer(
x_train, x_test, GloVe_DIR, MAX_NB_WORDS, MAX_SEQUENCE_LENGTH,
EMBEDDING_DIM)
del x_train
del x_test
gc.collect()
y_pr = []
History = []
score = []
if no_of_classes == 0:
#checking no_of_classes
#np.max(data)+1 will not work for one_hot encoding labels
number_of_classes = len(y_train[0])
print(number_of_classes)
else:
number_of_classes = no_of_classes
print(number_of_classes)
i = 0
while i < random_deep[0]:
# model_DNN.append(Sequential())
try:
print("DNN " + str(i))
filepath = "weights\weights_DNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
model_DNN, model_tmp = BuildModel.Build_Model_DNN_Text(
x_train_tfidf.shape[1], number_of_classes, sparse_categorical,
min_hidden_layer_dnn, max_hidden_layer_dnn, min_nodes_dnn,
max_nodes_dnn, random_optimizor, dropout)
model_history = model_DNN.fit(x_train_tfidf,
y_train,
validation_data=(x_test_tfidf,
y_test),
epochs=epochs[0],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
History.append(model_history)
model_tmp.load_weights(filepath)
if sparse_categorical:
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr_ = model_tmp.predict_classes(x_test_tfidf,
batch_size=batch_size)
y_pr.append(np.array(y_pr_))
score.append(accuracy_score(y_test, y_pr_))
else:
model_tmp.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr_ = model_tmp.predict(x_test_tfidf, batch_size=batch_size)
y_pr_ = np.argmax(y_pr_, axis=1)
y_pr.append(np.array(y_pr_))
y_test_temp = np.argmax(y_test, axis=1)
score.append(accuracy_score(y_test_temp, y_pr_))
# print(y_proba)
i += 1
del model_tmp
del model_DNN
except Exception as e:
print("Check the Error \n {} ".format(e))
print("Error in model", i, "try to re-generate another model")
if max_hidden_layer_dnn > 3:
max_hidden_layer_dnn -= 1
if max_nodes_dnn > 256:
max_nodes_dnn -= 8
try:
del x_train_tfidf
del x_test_tfidf
gc.collect()
except:
pass
i = 0
while i < random_deep[1]:
try:
print("RNN " + str(i))
filepath = "weights\weights_RNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
model_RNN, model_tmp = BuildModel.Build_Model_RNN_Text(
word_index, embeddings_index, number_of_classes,
MAX_SEQUENCE_LENGTH, EMBEDDING_DIM, sparse_categorical,
min_hidden_layer_rnn, max_hidden_layer_rnn, min_nodes_rnn,
max_nodes_rnn, random_optimizor, dropout)
model_history = model_RNN.fit(x_train_embedded,
y_train,
validation_data=(x_test_embedded,
y_test),
epochs=epochs[1],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
History.append(model_history)
if sparse_categorical:
model_tmp.load_weights(filepath)
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
y_pr_ = model_tmp.predict_classes(x_test_embedded,
batch_size=batch_size)
y_pr.append(np.array(y_pr_))
score.append(accuracy_score(y_test, y_pr_))
else:
model_tmp.load_weights(filepath)
model_tmp.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
y_pr_ = model_tmp.predict(x_test_embedded,
batch_size=batch_size)
y_pr_ = np.argmax(y_pr_, axis=1)
y_pr.append(np.array(y_pr_))
y_test_temp = np.argmax(y_test, axis=1)
score.append(accuracy_score(y_test_temp, y_pr_))
i += 1
del model_tmp
del model_RNN
gc.collect()
except:
print("Error in model", i, "try to re-generate another model")
if max_hidden_layer_rnn > 3:
max_hidden_layer_rnn -= 1
if max_nodes_rnn > 64:
max_nodes_rnn -= 2
gc.collect()
i = 0
while i < random_deep[2]:
try:
print("CNN " + str(i))
model_CNN, model_tmp = BuildModel.Build_Model_CNN_Text(
word_index, embeddings_index, number_of_classes,
MAX_SEQUENCE_LENGTH, EMBEDDING_DIM, sparse_categorical,
min_hidden_layer_cnn, max_hidden_layer_cnn, min_nodes_cnn,
max_nodes_cnn, random_optimizor, dropout)
filepath = "weights\weights_CNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
model_history = model_CNN.fit(x_train_embedded,
y_train,
validation_data=(x_test_embedded,
y_test),
epochs=epochs[2],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
History.append(model_history)
model_tmp.load_weights(filepath)
if sparse_categorical:
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
else:
model_tmp.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
y_pr_ = model_tmp.predict(x_test_embedded, batch_size=batch_size)
y_pr_ = np.argmax(y_pr_, axis=1)
y_pr.append(np.array(y_pr_))
if sparse_categorical:
score.append(accuracy_score(y_test, y_pr_))
else:
y_test_temp = np.argmax(y_test, axis=1)
score.append(accuracy_score(y_test_temp, y_pr_))
i += 1
del model_tmp
del model_CNN
gc.collect()
except:
print("Error in model", i, "try to re-generate an other model")
if max_hidden_layer_cnn > 5:
max_hidden_layer_cnn -= 1
if max_nodes_cnn > 128:
max_nodes_cnn -= 2
min_nodes_cnn -= 1
gc.collect()
y_proba = np.array(y_pr).transpose()
final_y = []
for i in range(0, y_proba.shape[0]):
a = np.array(y_proba[i, :])
a = collections.Counter(a).most_common()[0][0]
final_y.append(a)
if sparse_categorical:
F_score = accuracy_score(y_test, final_y)
F1 = precision_recall_fscore_support(y_test, final_y, average='micro')
F2 = precision_recall_fscore_support(y_test, final_y, average='macro')
F3 = precision_recall_fscore_support(y_test,
final_y,
average='weighted')
cnf_matrix = confusion_matrix(y_test, final_y)
# Compute confusion matrix
# Plot non-normalized confusion matrix
if plot:
classes = list(range(0, np.max(y_test) + 1))
Plot.plot_confusion_matrix(
cnf_matrix,
classes=classes,
title='Confusion matrix, without normalization')
# Plot normalized confusion matrix
Plot.plot_confusion_matrix(cnf_matrix,
classes=classes,
normalize=True,
title='Normalized confusion matrix')
else:
y_test_temp = np.argmax(y_test, axis=1)
F_score = accuracy_score(y_test_temp, final_y)
F1 = precision_recall_fscore_support(y_test_temp,
final_y,
average='micro')
F2 = precision_recall_fscore_support(y_test_temp,
final_y,
average='macro')
F3 = precision_recall_fscore_support(y_test_temp,
final_y,
average='weighted')
if plot:
Plot.RMDL_epoch(History)
print(y_proba.shape)
print("Accuracy of", len(score), "models:", score)
print("Accuracy:", F_score)
print("F1_Micro:", F1)
print("F1_Macro:", F2)
print("F1_weighted:", F3)
def Image_Classification(x_train,
y_train,
x_test,
y_test,
shape,
batch_size=128,
sparse_categorical=True,
random_deep=[3, 3, 3],
epochs=[500, 500, 500],
plot=False,
min_hidden_layer_dnn=1,
max_hidden_layer_dnn=8,
min_nodes_dnn=128,
max_nodes_dnn=1024,
min_hidden_layer_rnn=1,
max_hidden_layer_rnn=5,
min_nodes_rnn=32,
max_nodes_rnn=128,
min_hidden_layer_cnn=3,
max_hidden_layer_cnn=10,
min_nodes_cnn=128,
max_nodes_cnn=512,
random_state=42,
random_optimizor=True,
dropout=0.05):
np.random.seed(random_state)
G.setup()
y_proba = []
score = []
history_ = []
if sparse_categorical:
number_of_classes = np.max(y_train) + 1
else:
number_of_classes = np.shape(y_train)[0]
i = 0
while i < random_deep[0]:
try:
print("DNN ", i, "\n")
model_DNN, model_tmp = BuildModel.Build_Model_DNN_Image(
shape, number_of_classes, sparse_categorical,
min_hidden_layer_dnn, max_hidden_layer_dnn, min_nodes_dnn,
max_nodes_dnn, random_optimizor, dropout)
filepath = "weights\weights_DNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
history = model_DNN.fit(x_train,
y_train,
validation_data=(x_test, y_test),
epochs=epochs[0],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
history_.append(history)
model_tmp.load_weights(filepath)
if sparse_categorical == 0:
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
else:
model_tmp.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr = model_tmp.predict_classes(x_test, batch_size=batch_size)
y_proba.append(np.array(y_pr))
score.append(accuracy_score(y_test, y_pr))
i = i + 1
del model_tmp
del model_DNN
gc.collect()
except:
print("Error in model", i, "try to re-generate an other model")
if max_hidden_layer_dnn > 3:
max_hidden_layer_dnn -= 1
if max_nodes_dnn > 256:
max_nodes_dnn -= 8
i = 0
while i < random_deep[1]:
try:
print("RNN ", i, "\n")
model_RNN, model_tmp = BuildModel.Build_Model_RNN_Image(
shape, number_of_classes, sparse_categorical, min_nodes_rnn,
max_nodes_rnn, random_optimizor, dropout)
filepath = "weights\weights_RNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
history = model_RNN.fit(x_train,
y_train,
validation_data=(x_test, y_test),
epochs=epochs[1],
batch_size=batch_size,
verbose=2,
callbacks=callbacks_list)
model_tmp.load_weights(filepath)
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
history_.append(history)
y_pr = model_tmp.predict(x_test, batch_size=batch_size)
y_pr = np.argmax(y_pr, axis=1)
y_proba.append(np.array(y_pr))
score.append(accuracy_score(y_test, y_pr))
i = i + 1
del model_tmp
del model_RNN
gc.collect()
except:
print("Error in model", i, " try to re-generate another model")
if max_hidden_layer_rnn > 3:
max_hidden_layer_rnn -= 1
if max_nodes_rnn > 64:
max_nodes_rnn -= 2
# reshape to be [samples][pixels][width][height]
i = 0
while i < random_deep[2]:
try:
print("CNN ", i, "\n")
model_CNN, model_tmp = BuildModel.Build_Model_CNN_Image(
shape, number_of_classes, sparse_categorical,
min_hidden_layer_cnn, max_hidden_layer_cnn, min_nodes_cnn,
max_nodes_cnn, random_optimizor, dropout)
filepath = "weights\weights_CNN_" + str(i) + ".hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
history = model_CNN.fit(x_train,
y_train,
validation_data=(x_test, y_test),
epochs=epochs[2],
batch_size=batch_size,
callbacks=callbacks_list,
verbose=2)
history_.append(history)
model_tmp.load_weights(filepath)
model_tmp.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
y_pr = model_tmp.predict_classes(x_test, batch_size=batch_size)
y_proba.append(np.array(y_pr))
score.append(accuracy_score(y_test, y_pr))
i = i + 1
del model_tmp
del model_CNN
gc.collect()
except:
print("Error in model", i, " try to re-generate another model")
if max_hidden_layer_cnn > 5:
max_hidden_layer_cnn -= 1
if max_nodes_cnn > 128:
max_nodes_cnn -= 2
min_nodes_cnn -= 1
y_proba = np.array(y_proba).transpose()
print(y_proba.shape)
final_y = []
for i in range(0, y_proba.shape[0]):
a = np.array(y_proba[i, :])
a = collections.Counter(a).most_common()[0][0]
final_y.append(a)
F_score = accuracy_score(y_test, final_y)
F1 = f1_score(y_test, final_y, average='micro')
F2 = f1_score(y_test, final_y, average='macro')
F3 = f1_score(y_test, final_y, average='weighted')
cnf_matrix = confusion_matrix(y_test, final_y)
# Compute confusion matrix
np.set_printoptions(precision=2)
if plot:
# Plot non-normalized confusion matrix
classes = list(range(0, np.max(y_test) + 1))
Plot.plot_confusion_matrix(
cnf_matrix,
classes=classes,
title='Confusion matrix, without normalization')
Plot.plot_confusion_matrix(
cnf_matrix,
classes=classes,
normalize=True,
title='Confusion matrix, without normalization')
if plot:
Plot.RMDL_epoch(history_)
print(y_proba.shape)
print("Accuracy of", len(score), "models:", score)
print("Accuracy:", F_score)
print("F1_Micro:", F1)
print("F1_Macro:", F2)
print("F1_weighted:", F3)