def get_cnn(input_data, num_labels):
model = Sequential()
model.add(Conv2D(16, kernel_size=2, activation='relu', input_shape=input_data.shape))
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(Conv2D(128, kernel_size=3, activation='relu'))
model.add(Flatten())
model.add(Dense(num_labels, activation='softmax'))
opt = Adamax(learning_rate=LEARNING_RATE)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
return model
def get_stacked_cnn_lstm(input_data, num_labels):
model = Sequential()
model.add(TimeDistributed(Conv2D(16, kernel_size=3, activation='relu', input_shape=input_data.shape)))
model.add(TimeDistributed(Conv2D(64, kernel_size=5, activation='relu')))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(units=2048))
model.add(Dense(num_labels, activation='softmax'))
opt = Adamax(learning_rate=LEARNING_RATE)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
return model
def create_model():
checkpoint = ModelCheckpoint('sdr_model.h5',
monitor='accuracy',
verbose=1,
save_best_only=True,
mode='max')
callbacks_list = [checkpoint]
gray_data = np.load("npy_data/gray_dataset.npy")
color_data = np.load("npy_data/color_dataset.npy")
# img_pixel_dataset = np.load("npy_data/img_pixel_dataset.npy")
label = np.load("npy_data/label.npy")
# dataset = pre_processing.npy_dataset_concatenate(gray_data, color_data)
dataset = pre_processing.npy_dataset_concatenate(gray_data, color_data)
# corr_matrix = np.corrcoef(dataset)
# print(corr_matrix)
le = preprocessing.LabelEncoder()
label = le.fit_transform(label)
x_train, x_test, y_train, y_test = train_test_split(dataset,
label,
test_size=0.20,
shuffle=True)
model = Sequential()
model.add(Dense(14, input_dim=14, activation=None))
model.add(Dense(128, activation='tanh'))
model.add(Dense(256, activation='sigmoid'))
model.add(Dense(3, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train,
y_train,
epochs=150,
verbose=0,
batch_size=20,
shuffle=True,
callbacks=callbacks_list)
pred_y_test = model.predict_classes(x_test)
acc_model = accuracy_score(y_test, pred_y_test)
print("Prediction Acc model:", acc_model)
print("Org. Labels:", y_test[:30])
print("Pred Labels:", (pred_y_test[:30]))
# c_report = classification_report(y_test, pred_y_test, zero_division=0)
# print(c_report)
print("\n\n")
def create_model(embeddings_file: str, input_size, input_length, hidden_size):
"""
Create simple regression model with a single embedding layer.
:param embeddings_file: embeddings file to load
:param input_size: size of input layer
:param hidden_size: size of embeddings
:return: Keras model
"""
model = Sequential()
model.add(
Embedding(input_size,
hidden_size,
input_length=input_length,
name='embedding'))
model.add(keras.layers.Lambda(lambda x: keras.backend.sum(x, axis=1)))
#model.add(Flatten())
model.add(Dense(92, activation="sigmoid"))
if embeddings_file is not None:
embeddings = np.loadtxt(embeddings_file)
model.get_layer("embedding").set_weights([embeddings])
#model.summary()
return model
def create_model(embeddings_file: str, input_size, hidden_size, output_size):
"""
Create simple regression model with a single embedding layer.
:param embeddings_file: embeddings file to load
:param input_size: size of input layer
:param hidden_size: size of embeddings
:param output_size: size of output layer
:return: Keras model
"""
model = Sequential()
model.add(
Embedding(input_size, hidden_size, input_length=1, name='embedding'))
if embeddings_file is not None:
embeddings = np.loadtxt(embeddings_file)
model.get_layer("embedding").set_weights([embeddings])
model.add(Flatten())
model.add(Dense(output_size, activation="sigmoid"))
return model
# print(X_train.shape, X_val.shape, X_test.shape, y_train.shape, y_val.shape, y_test.shape)
# model = Sequential([
# Dense(100, activation='relu', input_shape=(57,)),
# Dense(40, activation='relu'),
# Dense(20, activation='relu'),
# Dense(1, activation='relu')
# ])
# model.compile(optimizer='adam',
# loss='mean_squared_logarithmic_error',
# metrics=['mean_squared_logarithmic_error'])
# hist = model.fit(X, y, epochs=100)
NNmodel = Sequential()
NNmodel.add(Dense(57, kernel_initializer='normal', activation='relu'))
NNmodel.add(Dense(100, kernel_initializer='normal', activation='relu'))
NNmodel.add(Dense(40, kernel_initializer='normal', activation='relu'))
NNmodel.add(Dense(20, kernel_initializer='normal', activation='relu'))
NNmodel.add(Dense(1, kernel_initializer='normal', activation='relu'))
NNmodel.compile(loss='mean_squared_logarithmic_error',
optimizer='adam',
metrics=['mean_squared_logarithmic_error'])
df_test['weather_4'] = 0
df_test = df_test[[
x for x in all_columns if x.startswith(tuple(train_columns))
]]
def create_model(input_shape:int, label_count:int):
"""Creates the neural network model"""
model = Sequential()
model.add(Conv2D(16, kernel_size=(4, 4), activation='relu', input_shape=input_shape))
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu'))
# 64 3x3 kernels
model.add(Conv2D(64, (3, 3), activation='relu'))
# Reduce by taking the max of each 2x2 block
model.add(MaxPooling2D(pool_size=(2, 2)))
# Dropout to avoid overfitting
model.add(Dropout(0.25))
# Flatten the results to one dimension for passing into our final layer
model.add(Flatten())
# A hidden layer to learn with
model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
# Another dropout
model.add(Dropout(0.5))
# Final categorization 0-9, A-z with softmax
model.add(Dense(label_count, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def MNIST_CNY19(classes, input_shape, weights=None):
model = Sequential()
model.add(
Convolution2D(40, (5, 5),
strides=(1, 1),
input_shape=input_shape,
activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(20, (5, 5), strides=(1, 1), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(320, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(80, activation='relu'))
model.add(Dense(40, activation='relu'))
model.add(Dense(classes, activation='softmax'))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
dataset['Price_Rise'] = np.where(
dataset['Close'].shift(-1) > dataset['Close'], 1, 0)
dataset.dropna(inplace=True)
X = dataset.iloc[:, 4:-1]
y = dataset.iloc[:, -1]
split = int(len(dataset) * 0.8)
X_train, X_test, y_train, y_test = X[:split], X[split:], y[:split], y[
split:]
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
y_train = y_train.to_numpy()
classifier = Sequential()
classifier.add(Dense(units=64, activation='relu', input_dim=X.shape[1]))
classifier.add(Dense(units=64, activation='relu'))
classifier.add(Dense(units=1, activation='sigmoid'))
classifier.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['accuracy'])
classifier.fit(X_train, y_train, batch_size=10, epochs=50)
y_pred = classifier.predict(X_test)
#y_pred = (y_pred > 0.5)
#y_pred = (np.round(y_pred * 2) - 1 )
y_pred = (2 * np.round(y_pred) - 1)
dataset['y_pred'] = np.NaN
dataset.iloc[(len(dataset) - len(y_pred)):, -1:] = y_pred
trade_dataset = dataset.dropna()
def _make_layers(self):
# Create the model
model = Sequential()
model.add(
Conv2D(32,
kernel_size=(3, 3),
activation='relu',
input_shape=(48, 48, 1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))
return model
def CIFAR_CNY19(classes, input_shape, weights=None):
model = Sequential()
model.add(
Convolution2D(40, (5, 5), strides=(1, 1), input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Convolution2D(20, (5, 5), strides=(1, 1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(240, activation='relu'))
# model.add(Dropout(0.5))
model.add(Dense(84, activation='relu'))
# model.add(Dropout(0.5))
model.add(Dense(classes, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
return model
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_datasets as tfds
from tensorflow_core.python.keras.layers import Embedding
from tensorflow_core.python.keras.models import Sequential
tfds.disable_progress_bar()
model = Sequential()
model.add(Embedding(1000, 64, input_length=10))
# the model will take as input an integer matrix of size (batch,
# input_length).
# the largest integer (i.e. word index) in the input should be no larger
# than 999 (vocabulary size).
# now model.output_shape == (None, 10, 64), where None is the batch
# dimension.
input_array = np.random.randint(1000, size=(32, 10))
model.compile('rmsprop', 'mse')
output_array = model.predict(input_array)
assert output_array.shape == (32, 10, 64)
embedding_layer = layers.Embedding(1000, 5)
def get_pen_cnn(input_data, num_labels):
model = Sequential()
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=input_data.shape))
model.add(MaxPooling2D(pool_size=2, strides=2))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(500, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_labels, activation='softmax'))
opt = Adam(learning_rate=.0003)
model.compile(loss='categorical_crossentropy', opt=opt, metrics=['accuracy'])
return model
def get_clstm(input_data, num_labels):
model = Sequential()
model.add(ConvLSTM2D(filters=16, kernel_size=(3, 3),
input_shape=input_data.shape,
padding='same', return_sequences=True))
model.add(BatchNormalization())
model.add(ConvLSTM2D(filters=64, kernel_size=(5, 5),
padding='same', return_sequences=False))
model.add(BatchNormalization())
model.add(Flatten())
model.add(Dense(num_labels, activation='softmax'))
opt = Adamax(learning_rate=LEARNING_RATE)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
return model
def get_cnn_adv(input_data, num_labels):
model = Sequential()
div = 4
model.add(Conv2D(64, kernel_size=(input_data.shape[0] // div, 1), activation='relu', input_shape=input_data.shape))
model.add(Conv2D(128, kernel_size=(div, 8), activation='relu'))
model.add(Flatten())
model.add(Dense(500, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_labels, activation='softmax'))
opt = Adamax(learning_rate=KeyConstants.ELR)
model.compile(loss='categorical_crossentropy', opt=opt, metrics=['accuracy'])
return model
