A-Simple-GAN
D
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# G.A.N\n",
"> Generative Adversial Network:\n",
"> - The main focus for GAN is to generate data from scratch.\n",
"> - It brings us closer to understanding intelligence.\n",
"- It trains two deep networks, called Generator and Discriminator, that compete and cooperate with each other. In the course of training, both networks eventually learn how to perform their tasks.\n",
"> > - The generator never actually sees examples from the domain and is adapted based on how well the discriminator performs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Original Article: How to Develop a 1D GAN](https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-1-dimensional-function-from-scratch-in-keras/)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"# The Import Statements:\n",
"from matplotlib import pyplot\n",
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.utils.vis_utils import plot_model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Defining a 1 D function:\n",
"> y=f(x)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def function_1D(x):\n",
" return x*x"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"inputs=np.arange(-0.5,0.6,0.1)\n",
"outputs=[function_1D(x) for x in inputs]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot the result\n",
"pyplot.plot(inputs, outputs)\n",
"pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#Defining random values\n",
"def generate_samples(n=100):\n",
" x1=np.random.rand(n)-0.5\n",
" x2=x1*x1\n",
" x1=x1.reshape(n,1)\n",
" x2=x2.reshape(n,1)\n",
" return np.hstack((x1,x2))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# generate samples and plotting them\n",
"data = generate_samples()\n",
"\n",
"pyplot.scatter(data[:, 0], data[:, 1])\n",
"pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Note:\n",
"> *a sample is comprised of a vector with two elements, one for the input and one for the output of our one-dimensional function.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The Discriminator\n",
"- The difference from a typical CNN is the absence of max-pooling in between layers.\n",
"- will have 1 hidden layer with 25 nodes.\n",
"- will use the ReLU activation function\n",
"- The output layer will have 1 node for the binary classification using the sigmoid activation function.\n",
"- Loss Function: Binary Cross Entropy\n",
"- Optimizer : Adam version of stochastic Gradient Descent"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Code for the Discriminator Unit:\n",
"def define_discriminator(n_inputs=2):\n",
"\tmodel = Sequential()\n",
"\tmodel.add(Dense(25, activation='relu', kernel_initializer='he_uniform', input_dim=n_inputs))\n",
"\tmodel.add(Dense(1, activation='sigmoid'))\n",
"\t# compile model\n",
"\tmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"\treturn model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_1 (Dense) (None, 25) 75 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 1) 26 \n",
"=================================================================\n",
"Total params: 101\n",
"Trainable params: 101\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# define the discriminator model\n",
"model = define_discriminator()\n",
"# summarize the model\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def generate_real_samples(n):\n",
" x1=np.random.rand(n)-0.5\n",
" x2=x1*x1\n",
" x1=x1.reshape(n,1)\n",
" x2=x2.reshape(n,1)\n",
" X= np.hstack((x1,x2))\n",
" y=np.ones((n,1))\n",
" return X,y"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def generate_fake_samples(n):\n",
"\t# generate inputs in [-1, 1]\n",
"\tX1 = -1 + np.random.rand(n) * 2\n",
"\t# generate outputs in [-1, 1]\n",
"\tX2 = -1 + np.random.rand(n) * 2\n",
"\t# stack arrays\n",
"\tX1 = X1.reshape(n, 1)\n",
"\tX2 = X2.reshape(n, 1)\n",
"\tX = np.hstack((X1, X2))\n",
"\t# generate class labels\n",
"\ty = np.zeros((n, 1))\n",
"\treturn X, y"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#training the discriminator model\n",
"def train_discriminator(model, n_epochs=1000, n_batch=128):\n",
"\thalf_batch = int(n_batch / 2)\n",
"\t# run epochs manually\n",
"\tfor i in range(n_epochs):\n",
"\t\t# generate real examples\n",
"\t\tX_real, y_real = generate_real_samples(half_batch)\n",
"\t\t# update model\n",
"\t\tmodel.train_on_batch(X_real, y_real)\n",
"\t\t# generate fake examples\n",
"\t\tX_fake, y_fake = generate_fake_samples(half_batch)\n",
"\t\t# update model\n",
"\t\tmodel.train_on_batch(X_fake, y_fake)\n",
"\t\t# evaluate the model\n",
"\t\t_, acc_real = model.evaluate(X_real, y_real, verbose=0)\n",
"\t\t_, acc_fake = model.evaluate(X_fake, y_fake, verbose=0)\n",
"\t\tprint(i, acc_real, acc_fake)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0.5 0.15625\n",
"1 0.546875 0.25\n",
"2 0.578125 0.21875\n",
"3 0.5625 0.21875\n",
"4 0.640625 0.234375\n",
"5 0.515625 0.40625\n",
"6 0.515625 0.46875\n",
"7 0.59375 0.390625\n",
"8 0.5625 0.46875\n",
"9 0.53125 0.4375\n",
"10 0.46875 0.484375\n",
"11 0.421875 0.53125\n",
"12 0.46875 0.484375\n",
"13 0.515625 0.5625\n",
"14 0.46875 0.6875\n",
"15 0.5625 0.515625\n",
"16 0.515625 0.53125\n",
"17 0.53125 0.5\n",
"18 0.484375 0.609375\n",
"19 0.40625 0.5\n",
"20 0.46875 0.671875\n",
"21 0.484375 0.59375\n",
"22 0.546875 0.609375\n",
"23 0.375 0.53125\n",
"24 0.421875 0.53125\n",
"25 0.34375 0.65625\n",
"26 0.421875 0.484375\n",
"27 0.421875 0.578125\n",
"28 0.328125 0.546875\n",
"29 0.359375 0.640625\n",
"30 0.4375 0.609375\n",
"31 0.296875 0.484375\n",
"32 0.296875 0.53125\n",
"33 0.515625 0.625\n",
"34 0.359375 0.59375\n",
"35 0.40625 0.65625\n",
"36 0.359375 0.6875\n",
"37 0.28125 0.578125\n",
"38 0.328125 0.578125\n",
"39 0.328125 0.671875\n",
"40 0.40625 0.671875\n",
"41 0.296875 0.515625\n",
"42 0.296875 0.578125\n",
"43 0.296875 0.625\n",
"44 0.34375 0.625\n",
"45 0.390625 0.671875\n",
"46 0.375 0.609375\n",
"47 0.34375 0.59375\n",
"48 0.359375 0.578125\n",
"49 0.359375 0.6875\n",
"50 0.34375 0.65625\n",
"51 0.375 0.515625\n",
"52 0.390625 0.609375\n",
"53 0.234375 0.625\n",
"54 0.359375 0.625\n",
"55 0.234375 0.59375\n",
"56 0.40625 0.6875\n",
"57 0.34375 0.765625\n",
"58 0.296875 0.609375\n",
"59 0.265625 0.59375\n",
"60 0.25 0.65625\n",
"61 0.390625 0.65625\n",
"62 0.265625 0.65625\n",
"63 0.46875 0.703125\n",
"64 0.40625 0.59375\n",
"65 0.359375 0.625\n",
"66 0.28125 0.625\n",
"67 0.3125 0.625\n",
"68 0.328125 0.640625\n",
"69 0.390625 0.625\n",
"70 0.28125 0.640625\n",
"71 0.328125 0.609375\n",
"72 0.296875 0.59375\n",
"73 0.125 0.703125\n",
"74 0.359375 0.765625\n",
"75 0.359375 0.65625\n",
"76 0.34375 0.5625\n",
"77 0.234375 0.671875\n",
"78 0.34375 0.6875\n",
"79 0.21875 0.734375\n",
"80 0.3125 0.78125\n",
"81 0.28125 0.6875\n",
"82 0.34375 0.640625\n",
"83 0.375 0.765625\n",
"84 0.34375 0.640625\n",
"85 0.328125 0.703125\n",
"86 0.21875 0.75\n",
"87 0.25 0.65625\n",
"88 0.28125 0.640625\n",
"89 0.3125 0.78125\n",
"90 0.234375 0.71875\n",
"91 0.328125 0.71875\n",
"92 0.25 0.671875\n",
"93 0.25 0.703125\n",
"94 0.265625 0.546875\n",
"95 0.296875 0.546875\n",
"96 0.328125 0.625\n",
"97 0.265625 0.75\n",
"98 0.296875 0.671875\n",
"99 0.265625 0.703125\n",
"100 0.15625 0.734375\n",
"101 0.234375 0.796875\n",
"102 0.25 0.71875\n",
"103 0.21875 0.828125\n",
"104 0.21875 0.828125\n",
"105 0.328125 0.765625\n",
"106 0.21875 0.671875\n",
"107 0.28125 0.84375\n",
"108 0.21875 0.796875\n",
"109 0.21875 0.78125\n",
"110 0.25 0.65625\n",
"111 0.25 0.8125\n",
"112 0.21875 0.859375\n",
"113 0.28125 0.796875\n",
"114 0.234375 0.625\n",
"115 0.21875 0.671875\n",
"116 0.234375 0.859375\n",
"117 0.265625 0.71875\n",
"118 0.21875 0.765625\n",
"119 0.234375 0.765625\n",
"120 0.234375 0.6875\n",
"121 0.234375 0.8125\n",
"122 0.296875 0.828125\n",
"123 0.25 0.796875\n",
"124 0.234375 0.890625\n",
"125 0.359375 0.765625\n",
"126 0.203125 0.75\n",
"127 0.328125 0.703125\n",
"128 0.203125 0.78125\n",
"129 0.265625 0.796875\n",
"130 0.3125 0.828125\n",
"131 0.21875 0.828125\n",
"132 0.25 0.828125\n",
"133 0.296875 0.84375\n",
"134 0.203125 0.875\n",
"135 0.34375 0.734375\n",
"136 0.421875 0.765625\n",
"137 0.15625 0.796875\n",
"138 0.359375 0.75\n",
"139 0.234375 0.75\n",
"140 0.421875 0.71875\n",
"141 0.28125 0.703125\n",
"142 0.1875 0.6875\n",
"143 0.21875 0.828125\n",
"144 0.25 0.890625\n",
"145 0.265625 0.9375\n",
"146 0.171875 0.8125\n",
"147 0.265625 0.84375\n",
"148 0.296875 0.90625\n",
"149 0.171875 0.875\n",
"150 0.21875 0.765625\n",
"151 0.25 0.875\n",
"152 0.25 0.90625\n",
"153 0.25 0.90625\n",
"154 0.328125 0.796875\n",
"155 0.296875 0.921875\n",
"156 0.234375 0.84375\n",
"157 0.25 0.875\n",
"158 0.1875 0.9375\n",
"159 0.234375 0.859375\n",
"160 0.234375 0.890625\n",
"161 0.28125 0.984375\n",
"162 0.21875 0.90625\n",
"163 0.21875 0.90625\n",
"164 0.265625 0.953125\n",
"165 0.28125 0.953125\n",
"166 0.328125 0.984375\n",
"167 0.28125 0.96875\n",
"168 0.25 0.953125\n",
"169 0.359375 0.921875\n",
"170 0.34375 1.0\n",
"171 0.328125 0.953125\n",
"172 0.296875 0.96875\n",
"173 0.359375 0.921875\n",
"174 0.28125 0.9375\n",
"175 0.375 0.984375\n",
"176 0.359375 0.96875\n",
"177 0.265625 0.984375\n",
"178 0.21875 0.96875\n",
"179 0.28125 0.9375\n",
"180 0.359375 1.0\n",
"181 0.34375 0.984375\n",
"182 0.40625 0.984375\n",
"183 0.296875 0.953125\n",
"184 0.34375 0.984375\n",
"185 0.3125 0.984375\n",
"186 0.359375 0.96875\n",
"187 0.375 0.984375\n",
"188 0.53125 0.953125\n",
"189 0.40625 0.921875\n",
"190 0.453125 0.953125\n",
"191 0.375 0.96875\n",
"192 0.609375 0.9375\n",
"193 0.40625 0.96875\n",
"194 0.4375 0.96875\n",
"195 0.390625 0.96875\n",
"196 0.40625 0.984375\n",
"197 0.4375 1.0\n",
"198 0.546875 0.953125\n",
"199 0.328125 1.0\n",
"200 0.328125 0.984375\n",
"201 0.328125 0.984375\n",
"202 0.453125 0.96875\n",
"203 0.34375 0.96875\n",
"204 0.34375 0.984375\n",
"205 0.359375 0.96875\n",
"206 0.359375 0.984375\n",
"207 0.421875 0.96875\n",
"208 0.5 0.984375\n",
"209 0.40625 1.0\n",
"210 0.390625 0.96875\n",
"211 0.546875 0.96875\n",
"212 0.390625 0.984375\n",
"213 0.5 0.921875\n",
"214 0.578125 0.96875\n",
"215 0.671875 0.9375\n",
"216 0.484375 0.984375\n",
"217 0.4375 0.90625\n",
"218 0.453125 0.96875\n",
"219 0.421875 0.953125\n",
"220 0.625 0.9375\n",
"221 0.5 0.953125\n",
"222 0.578125 0.984375\n",
"223 0.53125 0.9375\n",
"224 0.5625 0.984375\n",
"225 0.546875 0.984375\n",
"226 0.5625 0.96875\n",
"227 0.578125 0.984375\n",
"228 0.5625 0.953125\n",
"229 0.546875 0.90625\n",
"230 0.546875 0.921875\n",
"231 0.75 1.0\n",
"232 0.546875 1.0\n",
"233 0.671875 1.0\n",
"234 0.5625 0.96875\n",
"235 0.640625 0.984375\n",
"236 0.6875 0.921875\n",
"237 0.59375 0.90625\n",
"238 0.71875 0.921875\n",
"239 0.609375 1.0\n",
"240 0.59375 0.953125\n",
"241 0.65625 0.953125\n",
"242 0.640625 0.984375\n",
"243 0.6875 0.984375\n",
"244 0.546875 0.9375\n",
"245 0.71875 1.0\n",
"246 0.71875 0.953125\n",
"247 0.609375 1.0\n",
"248 0.671875 0.96875\n",
"249 0.625 0.953125\n",
"250 0.53125 0.921875\n",
"251 0.6875 0.984375\n",
"252 0.625 0.921875\n",
"253 0.65625 0.984375\n",
"254 0.625 0.9375\n",
"255 0.75 0.96875\n",
"256 0.609375 0.984375\n",
"257 0.640625 0.921875\n",
"258 0.6875 0.984375\n",
"259 0.671875 1.0\n",
"260 0.625 0.9375\n",
"261 0.65625 0.984375\n",
"262 0.703125 0.96875\n",
"263 0.734375 1.0\n",
"264 0.65625 0.984375\n",
"265 0.578125 0.9375\n",
"266 0.59375 0.96875\n",
"267 0.703125 0.921875\n",
"268 0.734375 1.0\n",
"269 0.734375 1.0\n",
"270 0.703125 0.984375\n",
"271 0.734375 0.984375\n",
"272 0.6875 0.953125\n",
"273 0.703125 0.96875\n",
"274 0.71875 1.0\n",
"275 0.65625 0.953125\n",
"276 0.75 0.921875\n",
"277 0.609375 0.984375\n",
"278 0.6875 1.0\n",
"279 0.6875 0.953125\n",
"280 0.765625 1.0\n",
"281 0.796875 1.0\n",
"282 0.734375 0.96875\n",
"283 0.671875 0.984375\n",
"284 0.578125 1.0\n",
"285 0.671875 0.953125\n",
"286 0.671875 0.984375\n",
"287 0.75 0.9375\n",
"288 0.734375 0.984375\n",
"289 0.71875 0.96875\n",
"290 0.75 0.96875\n",
"291 0.6875 0.984375\n",
"292 0.65625 0.9375\n",
"293 0.734375 0.984375\n",
"294 0.65625 0.953125\n",
"295 0.703125 0.96875\n",
"296 0.6875 0.984375\n",
"297 0.71875 0.984375\n",
"298 0.734375 0.953125\n",
"299 0.71875 0.9375\n",
"300 0.734375 0.984375\n",
"301 0.78125 0.953125\n",
"302 0.75 0.9375\n",
"303 0.75 0.953125\n",
"304 0.796875 0.96875\n",
"305 0.734375 1.0\n",
"306 0.71875 0.921875\n",
"307 0.796875 0.953125\n",
"308 0.84375 0.96875\n",
"309 0.75 0.96875\n",
"310 0.71875 1.0\n",
"311 0.828125 0.96875\n",
"312 0.8125 0.984375\n",
"313 0.78125 0.9375\n",
"314 0.84375 0.96875\n",
"315 0.6875 0.984375\n",
"316 0.84375 0.953125\n",
"317 0.8125 0.984375\n",
"318 0.765625 0.9375\n",
"319 0.71875 0.9375\n",
"320 0.703125 0.9375\n",
"321 0.671875 0.96875\n",
"322 0.703125 0.921875\n",
"323 0.765625 0.96875\n",
"324 0.703125 0.953125\n",
"325 0.828125 0.96875\n",
"326 0.828125 0.96875\n",
"327 0.78125 0.984375\n",
"328 0.8125 0.953125\n",
"329 0.796875 0.984375\n",
"330 0.765625 0.9375\n",
"331 0.890625 0.90625\n",
"332 0.8125 0.90625\n",
"333 0.8125 1.0\n",
"334 0.78125 0.96875\n",
"335 0.875 0.96875\n",
"336 0.84375 0.984375\n",
"337 0.796875 1.0\n",
"338 0.8125 0.984375\n",
"339 0.78125 0.9375\n",
"340 0.875 0.953125\n",
"341 0.8125 0.921875\n",
"342 0.796875 0.90625\n",
"343 0.796875 0.953125\n",
"344 0.8125 0.984375\n",
"345 0.890625 0.9375\n",
"346 0.828125 0.9375\n",
"347 0.78125 0.96875\n",
"348 0.90625 0.953125\n",
"349 0.828125 0.96875\n",
"350 0.796875 0.953125\n",
"351 0.734375 0.921875\n",
"352 0.796875 0.984375\n",
"353 0.765625 0.921875\n",
"354 0.8125 0.90625\n",
"355 0.765625 0.96875\n",
"356 0.84375 0.96875\n",
"357 0.875 0.953125\n",
"358 0.8125 0.90625\n",
"359 0.828125 0.96875\n",
"360 0.828125 0.953125\n",
"361 0.84375 0.984375\n",
"362 0.859375 0.9375\n",
"363 0.859375 0.953125\n",
"364 0.859375 0.9375\n",
"365 0.875 0.96875\n",
"366 0.875 0.953125\n",
"367 0.8125 0.953125\n",
"368 0.90625 0.9375\n",
"369 0.875 0.984375\n",
"370 0.9375 0.890625\n",
"371 0.875 0.9375\n",
"372 0.796875 0.921875\n",
"373 0.828125 0.921875\n",
"374 0.90625 0.921875\n",
"375 0.8125 0.9375\n",
"376 0.8125 0.90625\n",
"377 0.90625 0.921875\n",
"378 0.765625 1.0\n",
"379 0.78125 0.953125\n",
"380 0.78125 0.953125\n",
"381 0.875 0.953125\n",
"382 0.875 0.9375\n",
"383 0.84375 0.890625\n",
"384 0.796875 0.9375\n",
"385 0.859375 0.96875\n",
"386 0.8125 0.96875\n",
"387 0.90625 0.953125\n",
"388 0.921875 0.9375\n",
"389 0.890625 0.953125\n",
"390 0.96875 0.921875\n",
"391 0.84375 0.875\n",
"392 0.875 0.921875\n",
"393 0.828125 0.9375\n",
"394 0.921875 0.9375\n",
"395 0.890625 0.9375\n",
"396 0.921875 0.921875\n",
"397 0.890625 1.0\n",
"398 0.875 0.984375\n",
"399 0.921875 0.9375\n",
"400 0.828125 0.953125\n",
"401 0.828125 0.90625\n",
"402 0.9375 0.953125\n",
"403 0.8125 0.890625\n",
"404 0.953125 0.953125\n",
"405 0.9375 0.890625\n",
"406 0.90625 0.96875\n",
"407 0.890625 0.984375\n",
"408 0.875 0.9375\n",
"409 0.921875 0.984375\n",
"410 0.859375 0.96875\n",
"411 0.890625 0.953125\n",
"412 0.921875 0.96875\n",
"413 0.921875 1.0\n",
"414 0.921875 0.96875\n",
"415 0.875 0.90625\n",
"416 0.96875 0.96875\n",
"417 0.890625 0.921875\n",
"418 0.90625 0.953125\n",
"419 0.875 0.9375\n",
"420 0.875 0.921875\n",
"421 0.96875 0.953125\n",
"422 0.921875 0.96875\n",
"423 0.890625 0.875\n",
"424 0.84375 0.90625\n",
"425 0.921875 0.984375\n",
"426 0.953125 0.890625\n",
"427 1.0 0.90625\n",
"428 0.890625 0.9375\n",
"429 0.921875 0.9375\n",
"430 0.9375 0.953125\n",
"431 0.96875 0.953125\n",
"432 0.90625 0.9375\n",
"433 0.953125 0.875\n",
"434 0.953125 0.890625\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"435 0.96875 0.90625\n",
"436 0.9375 0.953125\n",
"437 0.96875 0.953125\n",
"438 0.953125 0.890625\n",
"439 0.921875 0.953125\n",
"440 0.953125 0.90625\n",
"441 0.90625 0.984375\n",
"442 0.96875 0.96875\n",
"443 0.984375 0.9375\n",
"444 0.921875 0.9375\n",
"445 0.96875 0.90625\n",
"446 0.921875 0.96875\n",
"447 0.890625 0.875\n",
"448 0.953125 0.953125\n",
"449 0.984375 0.953125\n",
"450 0.921875 0.953125\n",
"451 1.0 0.90625\n",
"452 0.984375 0.96875\n",
"453 1.0 0.90625\n",
"454 0.953125 0.953125\n",
"455 0.96875 0.9375\n",
"456 0.953125 0.90625\n",
"457 1.0 0.921875\n",
"458 0.984375 0.96875\n",
"459 0.984375 0.90625\n",
"460 0.984375 0.984375\n",
"461 1.0 0.921875\n",
"462 0.984375 0.953125\n",
"463 0.96875 0.921875\n",
"464 1.0 0.921875\n",
"465 0.984375 0.859375\n",
"466 1.0 0.90625\n",
"467 1.0 0.890625\n",
"468 1.0 0.90625\n",
"469 1.0 0.875\n",
"470 1.0 0.84375\n",
"471 1.0 0.875\n",
"472 0.984375 0.859375\n",
"473 1.0 0.859375\n",
"474 1.0 0.921875\n",
"475 0.984375 0.96875\n",
"476 1.0 0.875\n",
"477 1.0 0.890625\n",
"478 1.0 0.921875\n",
"479 1.0 0.890625\n",
"480 1.0 0.953125\n",
"481 1.0 0.90625\n",
"482 1.0 0.890625\n",
"483 1.0 0.90625\n",
"484 1.0 0.90625\n",
"485 1.0 0.890625\n",
"486 1.0 0.921875\n",
"487 1.0 0.96875\n",
"488 1.0 0.9375\n",
"489 1.0 0.921875\n",
"490 1.0 0.890625\n",
"491 1.0 0.890625\n",
"492 1.0 0.9375\n",
"493 1.0 0.90625\n",
"494 0.984375 0.9375\n",
"495 1.0 0.875\n",
"496 1.0 0.890625\n",
"497 1.0 0.9375\n",
"498 1.0 0.921875\n",
"499 0.984375 0.921875\n",
"500 1.0 0.921875\n",
"501 1.0 0.875\n",
"502 1.0 0.90625\n",
"503 1.0 0.953125\n",
"504 1.0 0.921875\n",
"505 1.0 0.96875\n",
"506 1.0 0.953125\n",
"507 1.0 0.921875\n",
"508 1.0 0.921875\n",
"509 1.0 0.890625\n",
"510 1.0 0.96875\n",
"511 1.0 0.921875\n",
"512 1.0 0.921875\n",
"513 1.0 0.859375\n",
"514 1.0 0.875\n",
"515 1.0 0.921875\n",
"516 1.0 0.9375\n",
"517 1.0 0.890625\n",
"518 1.0 0.90625\n",
"519 1.0 0.875\n",
"520 1.0 0.90625\n",
"521 1.0 0.9375\n",
"522 1.0 0.921875\n",
"523 1.0 0.875\n",
"524 1.0 0.875\n",
"525 1.0 0.921875\n",
"526 1.0 0.953125\n",
"527 1.0 0.90625\n",
"528 1.0 0.90625\n",
"529 1.0 0.90625\n",
"530 1.0 0.890625\n",
"531 1.0 0.90625\n",
"532 1.0 0.921875\n",
"533 1.0 0.921875\n",
"534 1.0 0.953125\n",
"535 1.0 0.875\n",
"536 1.0 0.90625\n",
"537 1.0 0.859375\n",
"538 1.0 0.890625\n",
"539 1.0 0.9375\n",
"540 1.0 0.9375\n",
"541 1.0 0.953125\n",
"542 1.0 0.890625\n",
"543 1.0 0.90625\n",
"544 1.0 0.84375\n",
"545 1.0 0.890625\n",
"546 1.0 0.890625\n",
"547 1.0 0.953125\n",
"548 1.0 0.9375\n",
"549 1.0 0.9375\n",
"550 1.0 0.890625\n",
"551 1.0 0.96875\n",
"552 1.0 0.921875\n",
"553 1.0 0.890625\n",
"554 1.0 0.90625\n",
"555 1.0 0.96875\n",
"556 1.0 0.921875\n",
"557 1.0 0.953125\n",
"558 1.0 0.890625\n",
"559 1.0 0.9375\n",
"560 1.0 0.9375\n",
"561 1.0 0.90625\n",
"562 1.0 0.9375\n",
"563 1.0 0.953125\n",
"564 1.0 0.875\n",
"565 1.0 0.90625\n",
"566 1.0 0.859375\n",
"567 1.0 0.875\n",
"568 1.0 0.921875\n",
"569 1.0 0.84375\n",
"570 1.0 0.890625\n",
"571 1.0 0.890625\n",
"572 1.0 0.953125\n",
"573 1.0 0.90625\n",
"574 1.0 0.921875\n",
"575 1.0 0.921875\n",
"576 1.0 0.921875\n",
"577 1.0 0.859375\n",
"578 1.0 0.9375\n",
"579 1.0 0.953125\n",
"580 1.0 0.921875\n",
"581 1.0 0.875\n",
"582 1.0 0.875\n",
"583 1.0 0.875\n",
"584 1.0 0.921875\n",
"585 1.0 0.890625\n",
"586 1.0 0.890625\n",
"587 1.0 0.890625\n",
"588 1.0 0.90625\n",
"589 1.0 0.875\n",
"590 1.0 0.90625\n",
"591 1.0 0.859375\n",
"592 1.0 0.90625\n",
"593 1.0 0.84375\n",
"594 1.0 0.859375\n",
"595 1.0 0.890625\n",
"596 1.0 0.90625\n",
"597 1.0 0.875\n",
"598 1.0 0.953125\n",
"599 1.0 0.875\n",
"600 1.0 0.859375\n",
"601 1.0 0.9375\n",
"602 1.0 0.875\n",
"603 1.0 0.859375\n",
"604 1.0 0.90625\n",
"605 1.0 0.90625\n",
"606 1.0 0.78125\n",
"607 1.0 0.828125\n",
"608 1.0 0.9375\n",
"609 1.0 0.921875\n",
"610 1.0 0.90625\n",
"611 1.0 0.953125\n",
"612 1.0 0.890625\n",
"613 1.0 0.9375\n",
"614 1.0 0.921875\n",
"615 1.0 0.796875\n",
"616 1.0 0.890625\n",
"617 1.0 0.921875\n",
"618 1.0 0.90625\n",
"619 1.0 0.890625\n",
"620 1.0 0.859375\n",
"621 1.0 0.921875\n",
"622 1.0 0.828125\n",
"623 1.0 0.875\n",
"624 1.0 0.953125\n",
"625 1.0 0.84375\n",
"626 1.0 0.9375\n",
"627 1.0 0.9375\n",
"628 1.0 0.859375\n",
"629 1.0 0.9375\n",
"630 1.0 0.90625\n",
"631 1.0 0.9375\n",
"632 1.0 0.921875\n",
"633 1.0 0.90625\n",
"634 1.0 0.90625\n",
"635 1.0 0.890625\n",
"636 1.0 0.78125\n",
"637 1.0 0.90625\n",
"638 1.0 0.890625\n",
"639 1.0 0.921875\n",
"640 1.0 0.75\n",
"641 1.0 0.875\n",
"642 1.0 0.875\n",
"643 1.0 0.90625\n",
"644 1.0 0.953125\n",
"645 1.0 0.921875\n",
"646 1.0 0.890625\n",
"647 1.0 0.859375\n",
"648 1.0 0.84375\n",
"649 1.0 0.90625\n",
"650 1.0 0.84375\n",
"651 1.0 0.84375\n",
"652 1.0 0.921875\n",
"653 1.0 0.90625\n",
"654 1.0 0.921875\n",
"655 1.0 0.921875\n",
"656 1.0 0.921875\n",
"657 1.0 0.84375\n",
"658 1.0 0.84375\n",
"659 1.0 0.90625\n",
"660 1.0 0.953125\n",
"661 1.0 0.890625\n",
"662 1.0 0.875\n",
"663 1.0 0.84375\n",
"664 1.0 0.84375\n",
"665 1.0 0.8125\n",
"666 1.0 0.859375\n",
"667 1.0 0.90625\n",
"668 1.0 0.90625\n",
"669 1.0 0.875\n",
"670 1.0 0.921875\n",
"671 1.0 0.84375\n",
"672 1.0 0.953125\n",
"673 1.0 0.84375\n",
"674 1.0 0.8125\n",
"675 1.0 0.84375\n",
"676 1.0 0.921875\n",
"677 1.0 0.953125\n",
"678 1.0 0.84375\n",
"679 1.0 0.859375\n",
"680 1.0 0.859375\n",
"681 1.0 0.90625\n",
"682 1.0 0.90625\n",
"683 1.0 0.90625\n",
"684 1.0 0.828125\n",
"685 1.0 0.875\n",
"686 1.0 0.9375\n",
"687 1.0 0.859375\n",
"688 1.0 0.859375\n",
"689 1.0 0.84375\n",
"690 1.0 0.953125\n",
"691 1.0 0.84375\n",
"692 1.0 0.859375\n",
"693 1.0 0.9375\n",
"694 1.0 0.890625\n",
"695 1.0 0.859375\n",
"696 1.0 0.875\n",
"697 1.0 0.875\n",
"698 1.0 0.9375\n",
"699 1.0 0.890625\n",
"700 1.0 0.921875\n",
"701 1.0 0.828125\n",
"702 1.0 0.84375\n",
"703 1.0 0.90625\n",
"704 1.0 0.859375\n",
"705 1.0 0.90625\n",
"706 1.0 0.921875\n",
"707 1.0 0.9375\n",
"708 1.0 0.84375\n",
"709 1.0 0.875\n",
"710 1.0 0.90625\n",
"711 1.0 0.90625\n",
"712 1.0 0.890625\n",
"713 1.0 0.921875\n",
"714 1.0 0.90625\n",
"715 1.0 0.890625\n",
"716 1.0 0.8125\n",
"717 1.0 0.875\n",
"718 1.0 0.890625\n",
"719 1.0 0.890625\n",
"720 1.0 0.90625\n",
"721 1.0 0.875\n",
"722 1.0 0.84375\n",
"723 1.0 0.9375\n",
"724 1.0 0.90625\n",
"725 1.0 0.84375\n",
"726 1.0 0.9375\n",
"727 1.0 0.875\n",
"728 1.0 0.8125\n",
"729 1.0 0.84375\n",
"730 1.0 0.9375\n",
"731 1.0 0.84375\n",
"732 1.0 0.875\n",
"733 1.0 0.921875\n",
"734 1.0 0.78125\n",
"735 1.0 0.78125\n",
"736 1.0 0.859375\n",
"737 1.0 0.84375\n",
"738 1.0 0.90625\n",
"739 1.0 0.859375\n",
"740 1.0 0.875\n",
"741 1.0 0.859375\n",
"742 1.0 0.890625\n",
"743 1.0 0.875\n",
"744 1.0 0.890625\n",
"745 1.0 0.859375\n",
"746 1.0 0.8125\n",
"747 1.0 0.859375\n",
"748 1.0 0.953125\n",
"749 1.0 0.84375\n",
"750 1.0 0.890625\n",
"751 1.0 0.796875\n",
"752 1.0 0.890625\n",
"753 1.0 0.796875\n",
"754 1.0 0.84375\n",
"755 1.0 0.890625\n",
"756 1.0 0.875\n",
"757 1.0 0.890625\n",
"758 1.0 0.921875\n",
"759 1.0 0.84375\n",
"760 1.0 0.875\n",
"761 1.0 0.859375\n",
"762 1.0 0.953125\n",
"763 1.0 0.890625\n",
"764 1.0 0.859375\n",
"765 1.0 0.84375\n",
"766 1.0 0.828125\n",
"767 1.0 0.8125\n",
"768 1.0 0.84375\n",
"769 1.0 0.859375\n",
"770 1.0 0.875\n",
"771 1.0 0.890625\n",
"772 1.0 0.90625\n",
"773 1.0 0.90625\n",
"774 1.0 0.953125\n",
"775 1.0 0.90625\n",
"776 1.0 0.890625\n",
"777 1.0 0.890625\n",
"778 1.0 0.921875\n",
"779 1.0 0.90625\n",
"780 1.0 0.90625\n",
"781 1.0 0.90625\n",
"782 1.0 0.796875\n",
"783 1.0 0.765625\n",
"784 1.0 0.921875\n",
"785 1.0 0.875\n",
"786 1.0 0.890625\n",
"787 1.0 0.90625\n",
"788 1.0 0.90625\n",
"789 1.0 0.890625\n",
"790 1.0 0.859375\n",
"791 1.0 0.890625\n",
"792 1.0 0.875\n",
"793 1.0 0.796875\n",
"794 1.0 0.859375\n",
"795 1.0 0.828125\n",
"796 1.0 0.890625\n",
"797 1.0 0.875\n",
"798 1.0 0.890625\n",
"799 1.0 0.8125\n",
"800 1.0 0.875\n",
"801 1.0 0.921875\n",
"802 1.0 0.90625\n",
"803 1.0 0.890625\n",
"804 1.0 0.90625\n",
"805 1.0 0.890625\n",
"806 1.0 0.90625\n",
"807 1.0 0.9375\n",
"808 1.0 0.90625\n",
"809 1.0 0.90625\n",
"810 1.0 0.859375\n",
"811 1.0 0.859375\n",
"812 1.0 0.890625\n",
"813 1.0 0.875\n",
"814 1.0 0.8125\n",
"815 1.0 0.875\n",
"816 1.0 0.921875\n",
"817 1.0 0.9375\n",
"818 1.0 0.890625\n",
"819 1.0 0.90625\n",
"820 1.0 0.921875\n",
"821 1.0 0.875\n",
"822 1.0 0.9375\n",
"823 1.0 0.90625\n",
"824 1.0 0.875\n",
"825 1.0 0.84375\n",
"826 1.0 0.875\n",
"827 1.0 0.8125\n",
"828 1.0 0.8125\n",
"829 1.0 0.890625\n",
"830 1.0 0.78125\n",
"831 1.0 0.890625\n",
"832 1.0 0.890625\n",
"833 1.0 0.84375\n",
"834 1.0 0.859375\n",
"835 1.0 0.875\n",
"836 1.0 0.890625\n",
"837 1.0 0.9375\n",
"838 1.0 0.953125\n",
"839 1.0 0.875\n",
"840 1.0 0.890625\n",
"841 1.0 0.78125\n",
"842 1.0 0.921875\n",
"843 1.0 0.875\n",
"844 1.0 0.84375\n",
"845 1.0 0.859375\n",
"846 1.0 0.828125\n",
"847 1.0 0.8125\n",
"848 1.0 0.921875\n",
"849 1.0 0.890625\n",
"850 1.0 0.84375\n",
"851 1.0 0.96875\n",
"852 1.0 0.90625\n",
"853 1.0 0.9375\n",
"854 1.0 0.875\n",
"855 1.0 0.890625\n",
"856 1.0 0.859375\n",
"857 1.0 0.890625\n",
"858 1.0 0.8125\n",
"859 1.0 0.875\n",
"860 1.0 0.875\n",
"861 1.0 0.875\n",
"862 1.0 0.859375\n",
"863 1.0 0.875\n",
"864 1.0 0.9375\n",
"865 1.0 0.796875\n",
"866 1.0 0.84375\n",
"867 1.0 0.90625\n",
"868 1.0 0.875\n",
"869 1.0 0.890625\n",
"870 1.0 0.953125\n",
"871 1.0 0.890625\n",
"872 1.0 0.90625\n",
"873 1.0 0.828125\n",
"874 1.0 0.96875\n",
"875 1.0 0.90625\n",
"876 1.0 0.84375\n",
"877 1.0 0.765625\n",
"878 1.0 0.96875\n",
"879 1.0 0.890625\n",
"880 1.0 0.890625\n",
"881 1.0 0.84375\n",
"882 1.0 0.890625\n",
"883 1.0 0.84375\n",
"884 1.0 0.859375\n",
"885 1.0 0.84375\n",
"886 1.0 0.859375\n",
"887 1.0 0.875\n",
"888 1.0 0.921875\n",
"889 1.0 0.90625\n",
"890 1.0 0.90625\n",
"891 1.0 0.859375\n",
"892 1.0 0.859375\n",
"893 1.0 0.9375\n",
"894 1.0 0.84375\n",
"895 1.0 0.859375\n",
"896 1.0 0.90625\n",
"897 1.0 0.9375\n",
"898 1.0 0.84375\n",
"899 1.0 0.890625\n",
"900 1.0 0.828125\n",
"901 1.0 0.90625\n",
"902 1.0 0.90625\n",
"903 1.0 0.859375\n",
"904 1.0 0.875\n",
"905 1.0 0.84375\n",
"906 1.0 0.875\n",
"907 1.0 0.875\n",
"908 1.0 0.859375\n",
"909 1.0 0.875\n",
"910 1.0 0.9375\n",
"911 1.0 0.796875\n",
"912 1.0 0.90625\n",
"913 1.0 0.859375\n",
"914 1.0 0.9375\n",
"915 1.0 0.875\n",
"916 1.0 0.859375\n",
"917 1.0 0.890625\n",
"918 1.0 0.890625\n",
"919 1.0 0.875\n",
"920 1.0 0.953125\n",
"921 1.0 0.921875\n",
"922 1.0 0.921875\n",
"923 1.0 0.890625\n",
"924 1.0 0.9375\n",
"925 1.0 0.859375\n",
"926 1.0 0.90625\n",
"927 1.0 0.921875\n",
"928 1.0 0.859375\n",
"929 1.0 0.859375\n",
"930 1.0 0.875\n",
"931 1.0 0.890625\n",
"932 1.0 0.890625\n",
"933 1.0 0.859375\n",
"934 1.0 0.859375\n",
"935 1.0 0.828125\n",
"936 1.0 0.96875\n",
"937 1.0 0.875\n",
"938 1.0 0.75\n",
"939 1.0 0.9375\n",
"940 1.0 0.828125\n",
"941 1.0 0.890625\n",
"942 1.0 0.828125\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"943 1.0 0.859375\n",
"944 1.0 0.875\n",
"945 1.0 0.921875\n",
"946 1.0 0.890625\n",
"947 1.0 0.921875\n",
"948 1.0 0.9375\n",
"949 1.0 0.953125\n",
"950 1.0 0.875\n",
"951 1.0 0.859375\n",
"952 1.0 0.890625\n",
"953 1.0 0.84375\n",
"954 1.0 0.875\n",
"955 1.0 0.875\n",
"956 1.0 0.90625\n",
"957 1.0 0.84375\n",
"958 1.0 0.828125\n",
"959 1.0 0.75\n",
"960 1.0 0.890625\n",
"961 1.0 0.875\n",
"962 1.0 0.921875\n",
"963 1.0 0.796875\n",
"964 1.0 0.875\n",
"965 1.0 0.90625\n",
"966 1.0 0.921875\n",
"967 1.0 0.796875\n",
"968 1.0 0.875\n",
"969 1.0 0.921875\n",
"970 1.0 0.828125\n",
"971 1.0 0.9375\n",
"972 1.0 0.890625\n",
"973 1.0 0.921875\n",
"974 1.0 0.875\n",
"975 1.0 0.9375\n",
"976 1.0 0.90625\n",
"977 1.0 0.953125\n",
"978 1.0 0.859375\n",
"979 1.0 0.828125\n",
"980 1.0 0.9375\n",
"981 1.0 0.953125\n",
"982 1.0 0.90625\n",
"983 1.0 0.828125\n",
"984 1.0 0.78125\n",
"985 1.0 0.90625\n",
"986 1.0 0.921875\n",
"987 1.0 0.90625\n",
"988 1.0 0.921875\n",
"989 1.0 0.890625\n",
"990 1.0 0.90625\n",
"991 1.0 0.890625\n",
"992 1.0 0.78125\n",
"993 1.0 0.859375\n",
"994 1.0 0.765625\n",
"995 1.0 0.8125\n",
"996 1.0 0.890625\n",
"997 1.0 0.890625\n",
"998 1.0 0.875\n",
"999 1.0 0.84375\n"
]
}
],
"source": [
"# define the discriminator model\n",
"model = define_discriminator()\n",
"# fit the model\n",
"train_discriminator(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"*The goal is to train a generator model, not a discriminator model, and that is where the complexity of GANs truly lies.*\" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Generator Model\n",
"*We will define a small latent space of five dimensions and use the standard approach in the GAN literature of using a Gaussian distribution for each variable in the latent space. We will generate new inputs by drawing random numbers from a standard Gaussian distribution, i.e. mean of zero and a standard deviation of one.*\n",
"\n",
"* Specs:\n",
"> * Single Hidden Layer with 5 nodes\n",
"> * ReLU activation Function\n",
"> * He weight initialization\n",
"> * Output layer will have 2 nodes+ will use linear activation function"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# define the generator model unit\n",
"def define_generator(latent_dim, n_outputs=2):\n",
"\tmodel = Sequential()\n",
"\tmodel.add(Dense(15, activation='relu', kernel_initializer='he_uniform', input_dim=latent_dim))\n",
"\tmodel.add(Dense(n_outputs, activation='linear'))\n",
"\treturn model"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_3\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_5 (Dense) (None, 15) 90 \n",
"_________________________________________________________________\n",
"dense_6 (Dense) (None, 2) 32 \n",
"=================================================================\n",
"Total params: 122\n",
"Trainable params: 122\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# define the discriminator model\n",
"model = define_generator(5)\n",
"# summarize the model\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# generate points in latent space as input for the generator\n",
"def generate_latent_points(latent_dim, n):\n",
"\t# generate points in the latent space\n",
"\tx_input = np.random.randn(latent_dim * n)\n",
"\t# reshape into a batch of inputs for the network\n",
"\tx_input = x_input.reshape(n, latent_dim)\n",
"\treturn x_input"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# use the generator to generate n fake examples and plot the results\n",
"def generate_fake_samples(generator, latent_dim, n):\n",
"\t# generate points in latent space\n",
"\tx_input = generate_latent_points(latent_dim, n)\n",
"\t# predict outputs\n",
"\tX = generator.predict(x_input)\n",
" # create class labels\n",
"\ty = np.zeros((n, 1))\n",
"\treturn X, y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*When the discriminator is good at detecting fake samples, the generator is updated more, and when the discriminator model is relatively poor or confused when detecting fake samples, the generator model is updated less.*"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# define the combined generator and discriminator model, for updating the generator\n",
"def define_gan(generator, discriminator):\n",
"\t# make weights in the discriminator not trainable\n",
"\tdiscriminator.trainable = False\n",
"\t# connect them\n",
"\tmodel = Sequential()\n",
"\t# add generator\n",
"\tmodel.add(generator)\n",
"\t# add the discriminator\n",
"\tmodel.add(discriminator)\n",
"\t# compile model\n",
"\tmodel.compile(loss='binary_crossentropy', optimizer='adam')\n",
"\treturn model"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# train the generator and discriminator\n",
"def train(g_model, d_model, gan_model, latent_dim, n_epochs=10000, n_batch=128, n_eval=2000):\n",
"\t# determine half the size of one batch, for updating the discriminator\n",
"\thalf_batch = int(n_batch / 2)\n",
"\t# manually enumerate epochs\n",
"\tfor i in range(n_epochs):\n",
"\t\t# prepare real samples\n",
"\t\tx_real, y_real = generate_real_samples(half_batch)\n",
"\t\t# prepare fake examples\n",
"\t\tx_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n",
"\t\t# update discriminator\n",
"\t\td_model.train_on_batch(x_real, y_real)\n",
"\t\td_model.train_on_batch(x_fake, y_fake)\n",
"\t\t# prepare points in latent space as input for the generator\n",
"\t\tx_gan = generate_latent_points(latent_dim, n_batch)\n",
"\t\t# create inverted labels for the fake samples\n",
"\t\ty_gan = np.ones((n_batch, 1))\n",
"\t\t# update the generator via the discriminator's error\n",
"\t\tgan_model.train_on_batch(x_gan, y_gan)\n",
"\t\t# evaluate the model every n_eval epochs\n",
"\t\tif (i+1) % n_eval == 0:\n",
"\t\t\tsummarize_performance(i, g_model, d_model, latent_dim)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# evaluate the discriminator and plot real and fake points\n",
"def summarize_performance(epoch, generator, discriminator, latent_dim, n=100):\n",
"\t# prepare real samples\n",
"\tx_real, y_real = generate_real_samples(n)\n",
"\t# evaluate discriminator on real examples\n",
"\t_, acc_real = discriminator.evaluate(x_real, y_real, verbose=0)\n",
"\t# prepare fake examples\n",
"\tx_fake, y_fake = generate_fake_samples(generator, latent_dim, n)\n",
"\t# evaluate discriminator on fake examples\n",
"\t_, acc_fake = discriminator.evaluate(x_fake, y_fake, verbose=0)\n",
"\t# summarize discriminator performance\n",
"\tprint(epoch, acc_real, acc_fake)\n",
"\t# scatter plot real and fake data points\n",
"\tpyplot.scatter(x_real[:, 0], x_real[:, 1], color='red')\n",
"\tpyplot.scatter(x_fake[:, 0], x_fake[:, 1], color='blue')\n",
"\tpyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1999 0.7400000095367432 0.4399999976158142\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# size of the latent space\n",
"latent_dim = 5\n",
"# create the discriminator\n",
"discriminator = define_discriminator()\n",
"# create the generator\n",
"generator = define_generator(latent_dim)\n",
"# create the gan\n",
"gan_model = define_gan(generator, discriminator)\n",
"# train model\n",
"train(generator, discriminator, gan_model, latent_dim)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
