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achaituachaitu
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adding the necessary codes
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Diff for: ‎codes/babble/babble_autoencoder_cnn_20db.ipynb

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Diff for: ‎codes/babble/babble_spec_cnn_v0.0_basiccode.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Got the input data\n",
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"(1540728, 129, 16, 1)\n",
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"conv2d_1 (Conv2D) (None, 125, 12, 16) 416 \n",
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"_________________________________________________________________\n",
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"conv2d_2 (Conv2D) (None, 123, 10, 32) 4640 \n",
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"_________________________________________________________________\n",
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"conv2d_3 (Conv2D) (None, 119, 6, 64) 51264 \n",
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"_________________________________________________________________\n",
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"conv2d_transpose_1 (Conv2DTr (None, 121, 8, 32) 18464 \n",
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"_________________________________________________________________\n",
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"conv2d_transpose_2 (Conv2DTr (None, 125, 12, 16) 12816 \n",
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"_________________________________________________________________\n",
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"conv2d_transpose_3 (Conv2DTr (None, 129, 16, 1) 401 \n",
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"=================================================================\n",
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"Total params: 88,001\n",
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"Trainable params: 88,001\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n",
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"None\n",
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"Train on 1980 samples, validate on 20 samples\n",
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"Epoch 1/10\n"
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]
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}
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],
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"source": [
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"import keras\n",
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"from keras.models import Sequential\n",
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"#from keras.layers import Dense, Dropout, Flatten\n",
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"from keras.layers import Conv2D, MaxPooling2D, Conv2DTranspose\n",
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"from keras import backend as K\n",
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"import pickle as pk\n",
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"batch_size = 1024\n",
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"epochs = 10\n",
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"import numpy as np\n",
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"import os\n",
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"cwd = os.getcwd()\n",
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"#x_train = os.path.join(cwd,'Noisy_TCDTIMIT/Babble/20/volunteers/01M/straightcam')\n",
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"#y_train = os.path.join(cwd,'Clean/volunteers/01M/straightcam')\n",
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"\n",
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"# pickle_train = open(\"noiseBabble_xtrain.pickle\",\"rb\")\n",
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"# x_train = pk.load(pickle_train)\n",
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"# pickle_trainlabel = open(\"cleanBabble.pickle\",\"rb\")\n",
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"# y_train = pk.load(pickle_trainlabel)\n",
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"\n",
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"x_train = np.load('Babbletrain.npy')\n",
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"y_train = np.load('Babblelabelclean.npy')\n",
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"\n",
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"print(\"Got the input data\")\n",
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"x_train = np.asarray(x_train)\n",
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"y_train = np.asarray(y_train)\n",
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"x_train = x_train.reshape(x_train.shape[0], 129, 16, 1)\n",
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"y_train = y_train.reshape(y_train.shape[0], 129, 16, 1)\n",
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"print(np.asarray(x_train).shape)\n",
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"\n",
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"model = Sequential()\n",
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"model.add(Conv2D(16, kernel_size=(5, 5),activation='relu',padding='valid',use_bias=True, kernel_initializer='glorot_uniform',bias_initializer='zeros',input_shape=(129,16,1)))\n",
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"model.add(Conv2D(32, (3, 3), activation='relu',padding='valid',use_bias=True, kernel_initializer='glorot_uniform',bias_initializer='zeros'))\n",
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"model.add(Conv2D(64, (5, 5), activation='relu',padding='valid',use_bias=True, kernel_initializer='glorot_uniform',bias_initializer='zeros'))\n",
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"#model.add(Conv2D(256, (7, 7), activation='relu',padding='valid',use_bias=True, kernel_initializer='glorot_uniform',bias_initializer='zeros'))\n",
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"#model.add(Conv2DTranspose(128, (3, 3), activation='relu',padding='valid',use_bias=True, kernel_initializer='glorot_uniform',bias_initializer='zeros'))\n",
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"model.add(Conv2DTranspose(32, (3, 3), activation='relu',padding='valid',use_bias=True, kernel_initializer='glorot_uniform',bias_initializer='zeros'))\n",
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"model.add(Conv2DTranspose(16, (5, 5), activation='relu',padding='valid',use_bias=True, kernel_initializer='glorot_uniform',bias_initializer='zeros'))\n",
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"model.add(Conv2DTranspose(1, (5, 5), activation='relu',padding='valid',use_bias=True, kernel_initializer='glorot_uniform',bias_initializer='zeros'))\n",
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"print(model.summary())\n",
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"\n",
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"#model.add(MaxPooling2D(pool_size=(2, 2)))\n",
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"#model.add(Dropout(0.25))\n",
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"#model.add(Flatten())\n",
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"#model.add(Dense(128, activation='relu'))\n",
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"#model.add(Dropout(0.5))\n",
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"#model.add(Dense(num_classes, activation='softmax'))\n",
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"\n",
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"model.compile(loss=keras.losses.mean_squared_error,\n",
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" optimizer=keras.optimizers.Adam(),\n",
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" metrics=['accuracy'])\n",
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"\n",
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"model.fit(np.asarray(x_train[0:2000]), np.asarray(y_train[0:2000]),\n",
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" batch_size=batch_size,\n",
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" epochs=epochs,\n",
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" validation_split = 0.01\n",
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" )\n",
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"\n",
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"#score = model.evaluate(x_train, y_train, verbose=0)\n",
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" \n",
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"#print('Test loss:', score[0])\n",
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"#print('Test accuracy:', score[1])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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