aniruddha27

# Streaming tweets from user timeline

user = "AnalyticsVidhya"
public_tweet = api.user_timeline(id=user,count=5)

for tweet in public_tweet:
    print("-->",tweet.text)

aniruddha27

# Streaming tweets from home timeline

public_tweet = api.home_timeline(count=5)

for tweet in public_tweet:
    print("-->",tweet.text)

aniruddha27

# Twitter API authentication

import tweepy

api_key =  # api_key
api_secret_key = # api_secret_key
access_token =  # access_token
access_token_secret =  # access_token_secret

# authorize the API Key

aniruddha27

import pandas as pd
import os
import shutil
from sklearn.model_selection import train_test_split

# Home directory
home_path = r'C:/Users/Dell/Desktop/Analytics Vidhya/ImageDataGenerator/emergency_vs_non-emergency_dataset/emergency_vs_non-emergency_dataset'

# Create train and validation directories
train_path = os.path.join(home_path,'train')

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# Images
train_images = df_train.loc[:,'image_name']
train_labels = df_train.loc[:,'category']

test_images = df_test.loc[:,'image_name']
test_labels = df_test.loc[:,'category']

# Train images
x_train = []
for i in train_images:

aniruddha27

# Compile
optim = RMSprop(learning_rate=0.00001)
model.compile(loss='categorical_crossentropy',
              optimizer=optim,
              metrics=['accuracy'])
# Fit
history = model.fit(x_train,y_train,
                    epochs=25,
                    validation_data=(x_test,y_test),
                    batch_size=32,

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# Split data into train-test data sets

X = df.loc[:,'image_names']
y = df.loc[:,'emergency_or_not']

# Split
train_x, val_x, train_y, val_y = train_test_split(X, y, 
                                                  test_size = 0.1, 
                                                  random_state = 27, 
                                                  stratify=y)

aniruddha27

# Augmenting on the fly with fit_generator()

# Directly use .flow()
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), 
                    epochs=epochs, # one forward/backward pass of training data
                    steps_per_epoch=x_train.shape[0]//batch_size, # number of images comprising of one epoch
                    validation_data=(x_test, y_test), # data for validation
                    validation_steps=x_test.shape[0]//batch_size)

# or use iterator from .flow_from_directory()

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# Epochs
epochs = 25
# Batch size
batch_size = 32

history = model.fit(train_datagen.flow(x_train,y_train,
                                       batch_size=batch_size, 
                                       seed=27,
                                       shuffle=False),
                    epochs=epochs,

aniruddha27

# Augmentation
train_datagen = ImageDataGenerator(rotation_range=5,  # rotation
                                   width_shift_range=0.2,  # horizontal shift
                                   zoom_range=0.2,  # zoom
                                   horizontal_flip=True,  # horizontal flip
                                   brightness_range=[0.2,0.8])  # brightness