def compile(name,
model: Sequential,
train_samples: pd.DataFrame,
validation_samples: pd.DataFrame,
gen,
type='img'):
# model.add(Reshape((-1, num_classes), name=RESHAPED))
size = 5
steps_per_epoch = len(train_samples) // size
validation_steps = len(validation_samples) // size
train_generator = gen(train_samples, type)(size, infinite=True)
validation_generator = gen(validation_samples, type)(size, infinite=True)
adam = optimizers.Adam(lr=0.0001)
model.compile(loss='categorical_crossentropy', optimizer=adam)
history_object = model.fit_generator(train_generator,
validation_data=validation_generator,
epochs=5,
callbacks=None,
validation_steps=validation_steps,
steps_per_epoch=steps_per_epoch)
model.save_weights(name)
# model.save('fcn_model.h5')
print(history_object.history.keys())
print('Loss')
print(history_object.history['loss'])
print('Validation Loss')
print(history_object.history['val_loss'])
def _build_model(self):
model = Sequential()
model.add(Dense(3, input_dim=2, activation='tanh'))
model.add(Dense(3, activation='tanh'))
model.add(Dense(self.env.action_space.n, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=self.alpha, decay=self.alpha_decay))
return model
def _build_model(self):
# Neural Net for Deep-Q learning Model
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss=self._huber_loss,
optimizer=Adam(lr=self.learning_rate))
return model
def _build(self, input):
"""
Builds the tiny yolo v2 network.
:param input: input image batch to the network
:return: logits output from network
"""
self.model = Sequential()
self.model.add(
Lambda(lambda x: x / 127.5 - 1., input_shape=self.input_shape))
self.model.add(Convolution2D())
return logits
def create_two_stream_classifier(
num_fc_neurons,
dropout_rate,
num_classes=24): # classifier_weights_path=None
classifier = Sequential()
classifier.add(
Dense(num_fc_neurons, name='fc7', input_shape=(num_fc_neurons * 2, )))
#classifier.add(BatchNormalization(axis=1, name='fc7_bn'))
classifier.add(Activation('relu', name='fc7_ac'))
classifier.add(Dropout(dropout_rate))
classifier.add(Dense(num_classes, activation='softmax',
name='predictions'))
return classifier
def __init__(self, learning_rate, layers, functions, optimizer_name,
beta=0.0, dropout=1.0):
self.n_input = layers[0]
self.n_hidden = layers[1:-1]
self.n_output = layers[-1]
self.model = Sequential()
if len(self.n_hidden) == 0:
# single layer
self.model.add(Dense(self.n_output, activation=functions[0],
kernel_regularizer=regularizers.l2(beta),
input_shape=(self.n_input,)))
elif len(self.n_hidden) == 1:
# hidden layer
self.model.add(Dense(self.n_hidden[0], activation=functions[0],
kernel_regularizer=regularizers.l2(beta),
input_shape=(self.n_input,)))
self.model.add(Dropout(dropout))
# output layer
self.model.add(Dense(self.n_output, activation=functions[1],
kernel_regularizer=regularizers.l2(beta)))
else:
# the first hidden layer
self.model.add(Dense(self.n_hidden[0], activation=functions[0],
kernel_regularizer=regularizers.l2(beta),
input_shape=(self.n_input,)))
self.model.add(Dropout(dropout))
# the second hidden layer
self.model.add(Dense(self.n_hidden[1], activation=functions[1],
kernel_regularizer=regularizers.l2(beta)))
self.model.add(Dropout(dropout))
# the output layer
self.model.add(Dense(self.n_output, activation=functions[2],
kernel_regularizer=regularizers.l2(beta)))
self.model.summary()
if optimizer_name == 'Adam': optimizer = Adam(learning_rate)
#self.model.compile(loss='mean_squared_error',
# optimizer=optimizer,
# metrics=['accuracy'])
self.model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
def add_softmax(model: Sequential) -> Sequential:
""" Append the softmax layers to the frontend or frontend + context net. """
# The softmax layer doesn't work on the (width, height, channel)
# shape, so we reshape to (width*height, channel) first.
# https://github.com/fchollet/keras/issues/1169
_, curr_width, curr_height, curr_channels = model.layers[-1].output_shape
model.add(Reshape((curr_width * curr_height, curr_channels)))
model.add(Activation('softmax'))
# Technically, we need another Reshape here to reshape to 2d, but TF
# the complains when batch_size > 1. We're just going to reshape in numpy.
# model.add(Reshape((curr_width, curr_height, curr_channels)))
return model
def lstm(self):
"""Build a simple LSTM network. We pass the extracted features from
our CNN to this model predomenently."""
# Model.
model = Sequential()
model.add(
LSTM(2048,
return_sequences=False,
input_shape=self.input_shape,
dropout=0.5))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(self.nb_classes, activation='softmax'))
return model
class TinyYoloV2(NetworkSkeleton):
"""
This class handles the building and the loss of the
tiny yolo v2 network.
"""
def __init__(self, config):
"""
Initializes class variables.
:param config: Contains the networks hyperparameters
"""
super.__init__(self)
self.config = config
self.network = None
self.loss = None
self.model = None
self.input_shape = config.input_shape
def _build(self, input):
"""
Builds the tiny yolo v2 network.
:param input: input image batch to the network
:return: logits output from network
"""
self.model = Sequential()
self.model.add(
Lambda(lambda x: x / 127.5 - 1., input_shape=self.input_shape))
self.model.add(Convolution2D())
return logits
def _loss(self):
"""
Calculates the loss of the network.
:return: loss
"""
raise NotImplemented
def __load_pretrained_network(self):
"""
def __init__(self, input_shape, lr=0.01, n_layers=2, n_hidden=8, rate_dropout=0.2, loss=risk_estimation):
print("initializing..., learing rate %s, n_layers %s, n_hidden %s, dropout rate %s." % (
lr, n_layers, n_hidden, rate_dropout))
self.model = Sequential()
self.model.add(Dropout(rate=rate_dropout, input_shape=(input_shape[0], input_shape[1])))
for i in range(0, n_layers - 1):
self.model.add(LSTM(n_hidden * 4, return_sequences=True, activation='tanh',
recurrent_activation='hard_sigmoid', kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal', bias_initializer='zeros',
dropout=rate_dropout, recurrent_dropout=rate_dropout))
self.model.add(LSTM(n_hidden, return_sequences=False, activation='tanh',
recurrent_activation='hard_sigmoid', kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal', bias_initializer='zeros',
dropout=rate_dropout, recurrent_dropout=rate_dropout))
self.model.add(Dense(1, kernel_initializer=initializers.glorot_uniform()))
# self.model.add(BatchNormalization(axis=-1, moving_mean_initializer=Constant(value=0.5),
# moving_variance_initializer=Constant(value=0.25)))
self.model.add(BatchNormalization(axis=-1))
self.model.add(Activation("relu(alpha=0., max_value=1.0)"))
opt = RMSprop(lr=lr)
self.model.compile(loss=loss,
optimizer=opt,
metrics=['accuracy'])
def grad_cam(input_model, image, category_index):
"""
Args: model to make predictions, image to predict, index of categories and
their predicted probabilities.
Constructs a colour map showing where the classifier puts the highest weight
for a given image in making its prediction.
Returns: numpy array of same dimension as image but instead displaying colours
according to where the classifier puts the most weight.
"""
model = Sequential()
model.add(input_model)
nb_classes = 10
target_layer = lambda x: target_category_loss(x, category_index, nb_classes
)
model.add(Lambda(target_layer))
loss = K.sum(model.layers[-1].output)
conv_output = model.layers[0].layers[
29].output #this needs changed depending on NN structure
grads = normalize(K.gradients(loss, conv_output)[0])
gradient_function = K.function([model.layers[0].input],
[conv_output, grads])
output, grads_val = gradient_function([image])
output, grads_val = output[0, :], grads_val[0, :, :, :]
weights = np.mean(grads_val, axis=(0, 1))
cam = np.ones(output.shape[0:2], dtype=np.float32)
for i, w in enumerate(weights):
cam += w * output[:, :, i]
cam = cv2.resize(cam, (224, 224))
cam = np.maximum(cam, 0)
heatmap = cam / np.max(cam)
#Return to BGR [0..255] from the preprocessed image
image = image[0, :]
image -= np.min(image)
image = np.minimum(image, 255)
cam = cv2.applyColorMap(np.uint8(255 * heatmap), cv2.COLORMAP_JET)
cam = np.float32(cam) + np.float32(image)
cam = 255.0 * cam / np.max(cam)
return np.uint8(cam)
def add_context(model: Sequential) -> Sequential:
""" Append the context layers to the frontend. """
model.add(ZeroPadding2D(padding=(33, 33)))
model.add(Conv2D(42, 3, activation='relu', name='ct_conv1_1'))
model.add(Conv2D(42, 3, activation='relu', name='ct_conv1_2'))
model.add(Conv2D(84, 3, 3, dilation_rate=(2, 2), activation='relu', name='ct_conv2_1'))
model.add(Conv2D(168, 3, dilation_rate=(4, 4), activation='relu', name='ct_conv3_1'))
model.add(Conv2D(336, 3, dilation_rate=(8, 8), activation='relu', name='ct_conv4_1'))
model.add(Conv2D(672, 3, dilation_rate=(16, 16), activation='relu', name='ct_conv5_1'))
model.add(Conv2D(672, 3, activation='relu', name='ct_fc1'))
model.add(Conv2D(21, 1, name='ct_final'))
return model
""" The `Sequential` model is a linear stack of layers. You can create a `Sequential` model by passing a list of layer instances to the constructor: """ """ Sequential (['class Sequential(Model):\n', Linear stack of layers.\n', '\n', ' Arguments:\n', ' layers: list of layers to add to the model.\n', '\n', ' # Note\n', ' The first layer passed to a Sequential model\n', ' should have a defined input shape. What that\n', ' means is that it should have received an `input_shape`\n', ' or `batch_input_shape` argument,\n', ' or for some type of layers (recurrent, Dense...)\n', ' an `input_dim` argument.\n', ' def __init__(self, layers=None, name=None):\n', ' self.layers = [] # Stack of layers.\n', ' self.model = None # Internal Model instance.\n', ' self.inputs = [] # List of input tensors\n', ' self.outputs = [] # List of length 1: the output tensor (unique).\n', ' self._trainable = True\n', ' self._initial_weights = None\n', '\n', ' # Model attributes.\n',
def generator_containing_discriminator(g, d):
model = Sequential()
model.add(g)
d.trainable = False
model.add(d)
return model
def discriminator_model():
model = Sequential()
model.add(Conv2D(64, (5, 5),padding='same',input_shape=(28, 28, 1)) )
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (5, 5)))
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
return model
def generator_model():
model = Sequential()
model.add(Dense(1024,input_dim=100 ))
model.add(Activation('tanh'))
model.add(Dense(128*7*7))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))
model.add(UpSampling2D(size=(2, 2)))
model.add(Conv2D(64, (5, 5), padding='same'))
model.add(Activation('tanh'))
model.add(UpSampling2D(size=(2, 2)))
model.add(Conv2D(1, (5, 5), padding='same'))
model.add(Activation('tanh'))
return model
def create_model():
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
values = reframed.values
n_train_hours = 365 * 24 # 1 year
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]
# split into input and outputs
train_X = train[:, :-1]
train_y = train[:, -1]
test_X = test[:, :-1]
test_y = test[:, -1]
# reshape input to be 3D [samples, timesteps, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
# design network
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X,
train_y,
epochs=50,
batch_size=72,
validation_data=(test_X, test_y),
verbose=2,
shuffle=False)
# plot history
pyplot.plot(history.history['loss'], label='Training Loss')
pyplot.plot(history.history['val_loss'], label='Validation Loss')
lr.fit(train_X, train_y) # no need for words into numbers, or one-hot
print("Accuracy = {:.2f}".format(lr.score(test_X, test_y))) # get metrics
def one_hot_encode_object_array(arr):
'''One hot encode a numpy array of objects (e.g. strings)'''
uniques, ids = np.unique(
arr, return_inverse=True) # convert 3 words into 0, 1, 2
return to_categorical(ids, len(uniques)) # convert 0, 1, 2 to one-hot
train_y_ohe = one_hot_encode_object_array(train_y)
test_y_ohe = one_hot_encode_object_array(test_y)
model = Sequential()
model.add(Dense(16, input_shape=(4, ))) # each sample has 4 features
model.add(Activation('sigmoid')) # add non-linearity to hidden layer 1
model.add(Dense(3)) # add another 3 neuron final layer
model.add(Activation('softmax')) # give it non-linearity as output
model.summary()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=["accuracy"])
model.fit(train_X,
train_y_ohe,
validation_split=0.2,
def mk_model():
model = Sequential()
model.add(
Convolution2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
input_shape=(IMAGE_SIZE, IMAGE_SIZE, 1),
kernel_initializer=kernel_initializer()))
model.add(LeakyReLU(alpha=.1))
model.add(
Convolution2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
kernel_initializer=kernel_initializer()))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(
Convolution2D(filters=64,
kernel_size=(3, 3),
kernel_initializer=kernel_initializer()))
model.add(LeakyReLU(alpha=0.1))
model.add(
Convolution2D(filters=64,
kernel_size=(3, 3),
kernel_initializer=kernel_initializer()))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(MaxoutDense(output_dim=256, nb_feature=4))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(LABEL_NUM, kernel_initializer='he_uniform'))
model.add(Activation('softmax'))
return model
def mk_model_with_bn():
model = Sequential()
model.add(
Convolution2D(filters=64,
kernel_size=(3, 3),
strides=(1, 1),
input_shape=(IMAGE_SIZE, IMAGE_SIZE, 1),
kernel_initializer=kernel_initializer()))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(
Convolution2D(filters=64,
kernel_size=(3, 3),
strides=(1, 1),
kernel_initializer=kernel_initializer()))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(
Convolution2D(filters=128,
kernel_size=(3, 3),
kernel_initializer=kernel_initializer()))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(
Convolution2D(filters=128,
kernel_size=(3, 3),
kernel_initializer=kernel_initializer()))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(
Convolution2D(filters=256,
kernel_size=(3, 3),
kernel_initializer=kernel_initializer()))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(
Convolution2D(filters=256,
kernel_size=(3, 3),
kernel_initializer=kernel_initializer()))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(256, kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(LABEL_NUM, kernel_initializer='he_normal'))
model.add(Activation('softmax'))
return model
### Multilayer Perceptron (MLP) for multi-class softmax classification:
"""
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.layers import Dense, Dropout, Activation
from tensorflow.contrib.keras.python.keras.optimizers import SGD
from tensorflow.contrib.keras.python.keras.utils import to_categorical
# Generate dummy data
import numpy as np
x_train = np.random.random((1000, 20))
y_train = to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)
x_test = np.random.random((100, 20))
y_test = to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
model = Sequential()
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
model.add(Dense(64, activation='relu', input_dim=20))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
# specify optimizer
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) # param adjust
model.compile(
loss='categorical_crossentropy', # multi-class
optimizer=sgd,
metrics=['accuracy'])
def get_frontend(input_width, input_height) -> Sequential:
model = Sequential()
# model.add(ZeroPadding2D((1, 1), input_shape=(input_width, input_height, 3)))
model.add(Conv2D(64, 3, activation='relu', name='conv1_1', input_shape=(input_width, input_height, 3)))
model.add(Conv2D(64, 3, activation='relu', name='conv1_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Conv2D(128, 3, activation='relu', name='conv2_1'))
model.add(Conv2D(128, 3, activation='relu', name='conv2_2'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Conv2D(256, 3, activation='relu', name='conv3_1'))
model.add(Conv2D(256, 3, activation='relu', name='conv3_2'))
model.add(Conv2D(256, 3, activation='relu', name='conv3_3'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Conv2D(512, 3, activation='relu', name='conv4_1'))
model.add(Conv2D(512, 3, activation='relu', name='conv4_2'))
model.add(Conv2D(512, 3, activation='relu', name='conv4_3'))
# Compared to the original VGG16, we skip the next 2 MaxPool layers,
# and go ahead with dilated convolutional layers instead
model.add(Conv2D(512, 3, dilation_rate=(2, 2), activation='relu', name='conv5_1'))
model.add(Conv2D(512, 3, dilation_rate=(2, 2), activation='relu', name='conv5_2'))
model.add(Conv2D(512, 3, dilation_rate=(2, 2), activation='relu', name='conv5_3'))
# Compared to the VGG16, we replace the FC layer with a convolution
model.add(Conv2D(4096, 7, dilation_rate=(4, 4), activation='relu', name='fc6'))
model.add(Dropout(0.5))
model.add(Conv2D(4096, 1, activation='relu', name='fc7'))
model.add(Dropout(0.5))
# Note: this layer has linear activations, not ReLU
model.add(Conv2D(21, 1, activation='linear', name='fc-final'))
# model.layers[-1].output_shape == (None, 16, 16, 21)
return model
[You can read more about stateful RNNs in the FAQ.](/getting-started/faq/#how-can-i-use-stateful-rnns)
"""
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.layers import LSTM, Dense
import numpy as np
data_dim = 16
timesteps = 8
num_classes = 10
batch_size = 32
# Expected input batch shape: (batch_size, timesteps, data_dim)
# Note that we have to provide the full batch_input_shape since the network is stateful.
# the sample of index i in batch k is the follow-up for the sample i in batch k-1.
model = Sequential()
model.add(
LSTM(
32,
return_sequences=True,
stateful=True, #input(32,8,16)
batch_input_shape=(batch_size, timesteps, data_dim))) #output(32,?,32)
model.add(LSTM(32, return_sequences=True, stateful=True)) # output(32,?,32)
model.add(LSTM(32, stateful=True)) # output (32,32)
model.add(Dense(10, activation='softmax')) # output (32, 10)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# Generate dummy training data
import numpy as np
import pylab as plt
#NN to create movie
from tensorflow.contrib.keras.python.keras.layers import ConvLSTM2D, BatchNormalization, Conv3D
from tensorflow.contrib.keras.python.keras.models import Sequential
seq = Sequential()
seq.add(
ConvLSTM2D(filters=40,
kernel_size=(3, 3),
input_shape=(None, 40, 40, 1),
padding='same',
return_sequences=True))
seq.add(BatchNormalization())
seq.add(
ConvLSTM2D(filters=40,
kernel_size=(3, 3),
padding='same',
return_sequences=True))
seq.add(BatchNormalization())
seq.add(
ConvLSTM2D(filters=40,
kernel_size=(3, 3),
padding='same',
return_sequences=True))
seq.add(BatchNormalization())
def main():
x_train, y_train, x_test, y_test = load_data()
model = Sequential()
model.add(
Conv2D(32,
kernel_size=(11, 11),
strides=4,
padding="same",
activation='relu',
input_shape=(48, 48, 1)))
model.add(MaxPooling2D(pool_size=(3, 3), strides=2, padding="valid"))
model.add(
Conv2D(32,
kernel_size=(5, 5),
strides=1,
padding="same",
activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=2, padding="valid"))
model.add(
Conv2D(32,
kernel_size=(3, 3),
strides=1,
padding="same",
activation='relu'))
model.add(
Conv2D(32,
kernel_size=(3, 3),
strides=1,
padding="same",
activation='relu'))
model.add(
Conv2D(32,
kernel_size=(3, 3),
strides=1,
padding="same",
activation='relu'))
model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(7, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train,
y_train,
batch_size=128,
epochs=5,
verbose=1,
validation_data=(x_test, y_test))
model.save(expanduser("~/emotion/alex_net.h5"))
accuracy, fbeta = test_model(model, x_test, y_test)
print("Accuracy: %s" % accuracy)
print("F-Beta: %s" % fbeta)
def simple_model():
model = Sequential()
model.add(
Convolution2D(filters=32,
kernel_size=(3, 3),
strides=(1, 1),
input_shape=(IMAGE_SIZE, IMAGE_SIZE, 1)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(filters=64, kernel_size=(3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(LABEL_NUM))
model.add(Activation('softmax'))
return model
"""
### MLP for binary classification:
"""
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.layers import Dense, Dropout, Activation
from tensorflow.contrib.keras.python.keras.optimizers import SGD
from tensorflow.contrib.keras.python.keras.utils import to_categorical
import numpy as np
# Generate dummy data
x_train = np.random.random((1000, 20))
y_train = np.random.randint(2, size=(1000, 1))
x_test = np.random.random((100, 20))
y_test = np.random.randint(2, size=(100, 1))
model = Sequential()
model.add(Dense(64, input_dim=20, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', # binary classification
optimizer='rmsprop',
metrics=['accuracy'])
hist = model.fit(x_train, y_train,
validation_split=0.2,
epochs=1,
batch_size=128)
np.max(y_train) + 1
num_classes = np.max(y_train) + 1
y_train = utils.to_categorical(y_train, num_classes)
y_test = utils.to_categorical(y_test, num_classes)
print('x_train shape :', x_train.shape)
print('x_test shape :', x_test.shape)
print('y_train shape :', y_train.shape)
print('y_test shape :', y_test.shape)
batch_size = 32
epochs = 5
model = Sequential()
model.add(Dense(512, input_shape=(max_words, )))
model.add(Activation('relu'))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_split=0.1)
from tensorflow.contrib.keras.python.keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
# y = np.array([[0],[1],[1],[0]])
# it is better to be replaced with true target function ^ or xor function
y = X[:, 0] ^ X[:, 1] # ^ is xor function
#######################################################
# Sequential can specify more activations layer from normal layers than Model
#######################################################
# check whether 3 models are the same or not
model = Sequential()
model.add(Dense(4, input_dim=2,
name='dense1')) # 2 nodes reaches 50% accuracy, 8 nodes 100%
model.add(Activation('relu', name='dense1_act'))
model.add(Dense(1, name='dense2'))
model.add(Activation('sigmoid', name='dense2_act')) # output shaped by sigmoid
model.summary() # see each layer of a model
# use Model instead of Sequential
input_tensor = Input(shape=(2, ), name='input')
hidden = Dense(4, activation='relu', name='dense1_relu')(input_tensor)
output = Dense(1, activation='sigmoid',
name='dense2_sigm')(hidden) # output shaped by sigmoid
model1 = Model(inputs=input_tensor, outputs=output)
model1.summary() # see each layer of a model
"""
